Nuclear Medicine : Radioactivity for Diagnosis and Therapy 9780470949450, 2012020183

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RETROMETABOLIC DRUG DESIGN AND TARGETING

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RETROMETABOLIC DRUG DESIGN AND TARGETING

Nicholas Bodor Center for Drug Discovery University of Florida Gainesville, Florida

Peter Buchwald Diabetes Research Institute and Molecular and Cellular Pharmacology Miller School of Medicine University of Miami Miami, Florida

A JOHN WILEY & SONS, INC., PUBLICATION

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Copyright © 2012 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic formats. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Bodor, Nicholas. Retrometabolic drug design and targeting / Nicholas Bodor, Peter Buchwald. p. ; cm. Includes bibliographical references and index. ISBN 978-0-470-94945-0 (cloth) I. Buchwald, Peter. II. Title. [DNLM: 1. Drug Design. 2. Drug Delivery Systems. 3. Drug Evaluation, Preclinical. 4. Pharmaceutical Preparations–metabolism. QV 745] 615.1 9–dc23 2012020183 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1

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CONTENTS Preface

ix

1

1

Introduction 1.1 New Drugs and Medical Progress 1 1.2 The Challenge of New Drug Discovery References 7

2

5

Mechanism of Drug Action: Basic Concepts

9

2.1 Pharmacodynamic Phase: Drug–Receptor Interactions 10 2.1.1 The Receptor Concept and Receptor Types 10 2.1.2 Ligand–Receptor Binding 12 2.1.3 Receptor Occupancy and Activation 16 2.2 Pharmacokinetic Phase: ADME 20 2.2.1 Drug Absorption and Distribution 20 2.2.2 Drug Metabolism and Excretion 22 2.2.3 Basic Pharmacokinetic Concepts 26 2.3 Structural Requirements: Keeping It “Drug-Like” 29 2.3.1 The Drug-Like Chemical Space 29 2.3.2 Oral Drugs: The Challenge of Bioavailability 31 References 33 3

The Drug Discovery and Development Process 3.1 Discovery Research 39 3.1.1 Prediscovery 39 3.1.2 Target Identification 41 3.1.3 Target Validation 42 3.1.4 Target-to-Hit and Hit-to-Lead Development 42 3.1.5 Early Distribution and Safety Tests 46 3.1.6 Lead Optimization 48 3.2 Preclinical Development 49 3.2.1 Preclinical Testing 49 3.2.2 Investigational New Drug Application and Safety

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CONTENTS

3.3 Clinical Development 51 3.3.1 Phase I Clinical Trials 51 3.3.2 Phase II Clinical Trials 51 3.3.3 Phase III Clinical Trials 52 3.4 Regulatory Approval and PostMarketing Development 53 3.4.1 New Drug Application and Regulatory Approval 53 3.4.2 Manufacturing 54 3.4.3 Postapproval Studies and Phase IV Trials 54 3.4.4 Patent Expiration and Generic Approval 54 3.5 Problems with the Current Paradigm 56 3.5.1 Decreasing R&D Efficiency 56 3.5.2 The Drug Discovery Process: Improvements Needed 62 References 64 4

Retrometabolic Drug Design 4.1 Design Principles 71 4.2 Terminology 72 4.2.1 Soft Drug vs. Hard Drug 72 4.2.2 Soft Drug vs. Prodrug 73 4.2.3 Chemical Delivery System vs. Prodrug References 74

5

71

73

Soft Drugs 5.1 Enzymatic Hydrolysis 78 5.1.1 Esterases 79 5.1.2 Interspecies Variability 81 5.1.3 Interorgan and Interindividual Variability 82 5.1.4 Mechanism: Catalytic Triad and Oxyanion Hole 83 5.1.5 Kinetics 84 5.1.6 Stereoselectivity 85 5.1.7 Activation Energy and Temperature Dependence 85 5.1.8 Structure–Metabolism Relationships 86 5.1.9 Rate-Influencing Role of the Alcohol or Acyl Side Chain 92 5.2 Soft Drug Approaches 93 5.3 Inactive Metabolite–Based Soft Drugs 96 5.3.1 Soft Beta-Blockers 97 5.3.2 Soft Opioid Analgetics: Remifentanil 106 5.3.3 Soft Corticosteroids 109 5.3.4 Soft Calcitriol (1␣,25–Dihydroxyvitamin D3 ) Analogs 137 5.3.5 Soft Estrogens 138 5.3.6 Soft ␤2 -Agonists 140

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CONTENTS

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5.3.7 Soft Psychostimulants 142 5.3.8 Soft Insecticides and Pesticides 144 5.3.9 Soft Anticholinergics: Inactive Metabolite–Based Approach 147 5.4 Soft Analogs 151 5.4.1 Soft Anticholinergics: Soft Quaternary Analogs 152 5.4.2 Soft Antimicrobials 154 5.4.3 Soft Antiarrhythmic Agents 156 5.4.4 Soft Serotonin Receptor Agonists: Naronapride 160 5.4.5 Soft Anticoagulants (Vitamin K Antagonists): Tecarfarin 162 5.4.6 Soft Angiotensin Converting Enzyme Inhibitors 164 5.4.7 Soft Dihydrofolate Reductase Inhibitors 165 5.4.8 Soft Calcineurin Inhibitors (Soft Immunosuppressants) 166 5.4.9 Soft Cytokine Modulators 169 5.4.10 Soft Phosphodiesterase 4 Inhibitors 171 5.4.11 Soft Matrix Metalloproteinase Inhibitors 173 5.4.12 Soft Cannabinoids 173 5.4.13 Soft Benzodiazepine Analogs: Remimazolam and Analogs 175 5.4.14 Soft Anesthetics 178 5.4.15 Soft Ca2+ Channel Blockers 182 5.5 Active Metabolite–Based Soft Drugs 184 5.6 Activated Soft Drugs 186 5.7 Pro-Soft Drugs 188 5.7.1 Pro-Soft Drugs of Natural Soft Drugs: Hormone Prodrugs 188 5.7.2 Pro-Soft Drugs of Peptidyl Boronic Acid Derivatives 188 5.8 Computer-Aided Design 191 5.8.1 Computer-Aided Soft Drug Design 192 5.8.2 Predicting Molecular Properties 194 5.8.3 Molecular Size 194 5.8.4 Lipophilicity: Octanol/Water Partition Coefficient 196 5.8.5 Water Solubility 205 5.8.6 Structure Generation 206 5.8.7 Candidate Ranking 206 5.8.8 Hydrolytic Lability 208 5.8.9 Illustrations 209 5.9 Soft Drugs: Summary 215 References 215 6

Chemical Delivery Systems 6.1 Enzymatic Physicochemical-Based (Brain-Targeting) CDSs

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6.1.1 The Challenge of Brain Targeting 260 6.1.2 The Blood–Brain Barrier 260 6.1.3 Brain-Targeting Drug Delivery Approaches 263 6.1.4 Brain-Targeting CDSs: Details 274 6.1.5 Quantifying Delivery 282 6.1.6 Genesis of the CDS Concept: Pro-2-PAM 284 6.1.7 Berberine 288 6.1.8 Dopamine CDS 290 6.1.9 Zidovudine (AZT) CDS 292 6.1.10 Other Antiviral and Antiretroviral CDSs 294 6.1.11 Anticancer CDSs 299 6.1.12 Other Brain-Targeting CDSs 302 6.1.13 Estradiol CDS 307 6.1.14 Cyclodextrin Complexes 328 6.1.15 Molecular Packaging 331 6.2 Site-Specific Enzyme-Activated (Eye-Targeting) CDSs 346 6.2.1 The Challenge of Ocular Targeting 346 6.2.2 Potential Therapeutic Applications: Glaucoma 347 6.2.3 Eye-Targeting CDSs: Design Principles 348 6.2.4 Oxime and Methoxime Analogs of Beta-Blockers 350 6.3 Receptor-Based Transient Anchor-Type CDSs 357 References 358 Conclusions

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Index

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PREFACE The discovery and widespread introduction of new, highly effective drugs was probably one of the most important transformative forces of the twentieth century, as it dramatically altered the way of life in all industrialized nations. Nevertheless, rational drug design, which would allow the development of effective new pharmaceutical agents with minimal side effects on as rational a basis as possible, is still an elusive goal. Unfortunately, our understanding of biological systems and their complexities is far from sufficient, and a few success stories of rational designs on the basis of the molecular mechanism of action overshadow the many more unexpected failures of projects that were also initiated on the basis of similarly plausible rationales. Contrary to the quite predictable and steady rate of development of technological and engineering fields, the efficiency and rate of new drug introductions has been steadily declining since the 1950s. There are several reasons for this, including the increasing regulatory burden, the expectation that any new drug will outperform all existing ones (many of which are highly effective), the unprecedented need for highly multidisciplinary approaches, the inability of the increasingly few and increasingly large organizations left in the pharmaceutical/biotechnology field to carry out truly innovative research, or the fact that many new technical developments ultimately failed to materialize in an increased NCE (new chemical entity) output. For example, an analysis of drugs launched in 2000 revealed that not only did combinatorial chemistry and high-throughput screening have no significant impact, but most of the new drugs launched were, in fact, derived by modification of known drug structures or published lead molecules. It has even been suggested that the new techniques may be generating bigger haystacks as opposed to more needles. During the drug discovery and development process, the identified leads generally have to undergo structural optimization to improve their activity and specificity. Typically, this process is focused on increasing the pharmacological potency, while side effect and toxicity issues are ignored at this stage. Consequently, large numbers of promising new drug candidates have to be discarded later in the development process when unacceptable toxicity or unavoidable side effects are encountered. Because side effects are often closely related to the intrinsic receptor affinity responsible for the desired activity, and because metabolism often generates multiple new structures that can have quantitatively or qualitatively different types of biological activity (including enhanced toxicity), rational drug design processes need to address all these issues from the beginning and need to integrate them thoroughly. The focus should not be on increasing activity, but on increasing the therapeutic index (TI), which is usually

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PREFACE

defined as the ratio between the median toxic dose (TD50 ) and the median effective dose (ED50 ) and reflects activity, selectivity, and margin of safety. To overcome these problems, metabolic and targeting considerations should be integrated into the drug discovery and development process from the very beginning. One needs to be aware of the fact that for any given drug, metabolic conversion can generate multiple metabolites that will have various activity and toxicity levels and that will be present at the different sites together with the original drug at varying concentrations. Hence, the overall activity and toxicity of any drug are, in fact, a combination of the intrinsic activity and toxicity of the original drug with those of all the metabolites created but not yet eliminated. The importance of drug metabolism in causing toxicity via reactive metabolites is finally being recognized, and structural alerts are being used increasingly in an attempt to minimize possible adverse drug reactions caused by reactive metabolites. However, just as activity considerations are built into the molecular structure of new drug candidates, the route of metabolic inactivation should also be built into the structure to avoid the formation of potentially toxic metabolites by design. Here we describe general drug design and targeting approaches developed during the past 40 or so years that represent systematic methodologies that thoroughly integrate structure–activity and structure–metabolism relationships and are aimed to design safe, locally active compounds with an improved therapeutic index. They are integrated under the retrometabolic drug design and targeting concept, a terminology selected in analogy to E. J. Corey’s well-known retrosynthetic concept used to design synthetic routes for complex natural products. Retrometabolic drug design approaches include two distinct methods aimed at designing soft drugs (SDs) and chemical delivery systems (CDSs), respectively. It is important to note that whereas both SDs and CDSs require designed-in enzymatic reactions to fulfill their drug targeting roles, they are at somewhat opposing ends of the spectrum: SDs are active as administered and are designed to be predictably metabolized by design into inactive species, while CDSs are inactive as administered and sequential enzymatic reactions provide the differential distribution and the ultimate release of the active drug. The first public exposure of some of these ideas took place in an IUPAC–IUPHAR Symposium in 1981 in Noordwijkerhout, The Netherlands. In a half-day open forum featuring two opposing ideas, one by the outstanding Dutch scientist E. J. Ari¨ens, who advocated the design of nonmetabolizable drugs, which can be called hard drugs, and the other by N. Bodor, who advocated the design of predictably and safely metabolizable drugs, which are now designated as soft drugs; the pros and cons were presented and discussed. While it became evident that it is virtually impossible to design drugs that do not metabolize at all (unless going to pharmacokinetic extremes), it was demonstrated that the second approach is quite general and that it indeed can lead to an improved therapeutic index. Subsequently, specific methods were developed for both the design of safe soft drugs and for the design of different organ-targeting chemical delivery systems. The application of these principles has already resulted in several Food and Drug Administration–approved marketed drugs.

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PREFACE

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In the present work, following a brief overview of the basic concepts of the mechanism of drug action as well as of the main phases of the drug discovery and development process, the general classes of soft drugs are presented with specific examples. In many cases, the relevant medicinal chemistry, pharmacokinetic, and pharmacodynamics aspects are discussed in detail. The most successful concepts are the inactive metabolite, soft analog, and active metabolite approaches. Subsequently, various CDS approaches are discussed, including those designed to provide brain targeting via a 1,4-dihydrotrigonelline  trigonelline (dihydropyridine  pyridinium) redox targetor system using a sequential metabolism approach as well as those designed to provide eye targeting via an oxime-type targetor. As a truly rational drug design system, the structural transformation rules needed to design metabolites and virtual soft drug libraries, respectively, are well defined and specific; therefore, the design process can to a large extent be computerized, and virtually any lead compound can be converted into a corresponding virtual soft drug library. To assist in the selection of the best candidates for synthesis and activity testing, the virtual structures can subsequently be ranked using molecular properties and metabolic rates predicted based on molecular descriptors calculated from the structure alone using semiempirical (e.g., AM1) quantum chemical methods.

ACKNOWLEDGMENTS None of the accomplishments of the senior author’s laboratory would have been possible without the contribution of some 150 graduate students, postdoctoral fellows, visiting scientists, and collaborators at the Center for Drug Discovery, University of Florida, whose work throughout the years is gratefully acknowledged. The work of the some 300 scientists, technicians, and staff employed by the Institute of Drug Research during the leadership of the senior author is also gratefully acknowledged. During all these years, collaborations and many invaluable discussions with mentors, co-workers, and friends also contributed significantly to the development and applications of all these concepts and ideas. The defining influences of Professors Michael J. S. Dewar, Takeru Higuchi, Michael Schwartz, Tsuneji Nagai, and Yuichi Sugiyama are acknowledged first. We are also grateful for the long-time help and support received from collaborators and co-workers such as Drs. Marcus E. Brewster, Thorsteinn Loftsson, James J. Kaminski, Efraim Shek, Emil Pop, John Howes, Hassan H. Farag, Hartmut Derendorf, G¨unther Hochhaus, Emy (Whei-Mei) Wu, Teruo Murakami, G´abor Somogyi, Sung-Hwa Yoon, Amy Buchwald, Fubao Ji, Istv´an Szel´enyi, Antal Simay, Katalin Horv´ath, Istv´an Kurucz, Zolt´an Zubovics, M´arta P´atfalusi, and many others. Daily lunch discussions through a six-year period with Phillip Frost, Chairman of Ivax Corporation and of Teva Pharmaceutical Industries, provided invaluable close insight into the working of the pharmaceutical industry and regulatory agencies. As always, all this work would not have been possible without the ongoing support and love of our families: Sheryl, Nicole, and Erik Bodor and Amy, Zoltan, and Zsuzsa Buchwald, respectively. Finally, let us note that most of the ideas, concepts,

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PREFACE

methods, and applications presented here are discussed regularly during a biannual Retrometabolic Drug Design and Targeting international scientific symposium series, which was started in 1997 and has its ninth conference scheduled for 2013 in Orlando, Florida. Nicholas Bodor Peter Buchwald

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Introduction NEW DRUGS AND MEDICAL PROGRESS

The tremendous medical progress of the twentieth century, which has probably surpassed the progress during the rest of human history combined, was driven primarily by the progress in drug research and discovery [1–4]. Introduction of effective new drugs can provide enormous therapeutic benefits, and sometimes can even create entirely new therapeutic fields. Whereas even the best physician can help only a very limited number of patients during an entire lifetime—probably a few thousands at best—a new drug may help millions and in some cases may even help establish an entirely new therapeutic area. In today’s developed industrial societies, we have already became so accustomed to many medical treatments which were real breakthroughs at their introduction that it is difficult to imagine what life could have been like before their introduction. Some of the more important ones include (Figure 1-1), for example (shown with their year of introduction in the United States) [5,6]:

r Morphine (1-1; ca.1806): the most abundant alkaloid in opium and a potent

r r r r

opiate analgesic isolated in the early nineteenth century and first marketed by Merck starting in 1827; still the gold standard analgesic used to relieve severe or agonizing pain and suffering Aspirin (1-2; ca.1899): prepared by Felix Hoffman and Arthur Eichengr¨un at Bayer in an attempt to find a salicylic acid derivative that causes less gastric irritation but maintains its anti-inflammatory properties; Arsphenamine (1-3; 1910): the first modern chemotherapeutic agent; also known as Salvarsan or 606; discovered by Sahachiro Hata and Paul Ehrlich in a rational and focused synthetic screening effort; used to treat syphilis and trypanosomiasis Insulin (1-4; 1922): the first lifesaving miracle drug; resulting from the work of Frederick Banting, Charles Best, John MacLeod, and others at the University of Toronto; completely altered the perspective of type 1 diabetes mellitus patients Sulfamidochrysoidine (1-5; ca.1935): the first effective sulfa drug (prontosil); resulting from the work of Gerhardt Domagk with azo dyes that became the first commercially available antibacterial and began the era of antimicrobial chemotherapy

Retrometabolic Drug Design and Targeting, First Edition. Nicholas Bodor and Peter Buchwald.
C 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc.

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INTRODUCTION

FIGURE 1-1. Some of the important drugs that provided significant therapeutic improvements at the time of their introduction (shown in approximate chronological order). For each drug, its year of U.S. market approval (or an equivalent estimate) and its main therapeutic category are also shown. During the twentieth century, these drugs completely altered the way of life in all industrialized nations.

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NEW DRUGS AND MEDICAL PROGRESS

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r Penicillin (1-6; 1928–1948): the powerful antibacterial miracle drug; discovered r

r

r

r r r r r r

r

r

accidentally by Alexander Fleming and then resurrected and produced in large quantities by Howard Florey and Boris Ernst Chain during World War II Methotrexate (1-7; 1950): a folic acid analog and a dihydrofolate reductase inhibitor; resulting from the work of Sidney Farber at Harvard Medical School, Yellapragada Subbarao at Lederle, and others; one of the earliest successful anticancer agents and the mainstay of leukemia chemotherapy Hydrocortisone (1-8; cortisol; 1952): a glucocorticoid steroid hormone synthesized by the adrenal glands that produces potent anti-inflammatory and immunesuppressive effects; discovered in the 1940s mainly by Philip Showalter Hench, Edward Calvin Kendall, and Tadeusz Reichstein during their work on hormones of the adrenal cortex Chlorpromazine (1-9; 1953): synthesized by Paul Charpentier at Laboratoires Rhˆone-Poulenc as part of a search for new antihistamines and promoted for psychiatric use mainly by Henri Laborit, so that the entire field of today’s psychopharmacology was, in fact, built on the foundation of a poor antihistamine Norethindrone (1-10; 1960): the first orally highly active progestin; synthesized by Carl Djerassi, George Rosenkranz, and co-workers at Syntex; ushered in the era of oral contraception Diazepam (1-11; 1963): a follow-up benzodiazepine to chlordiazepoxide; synthesized by Leo Sternbach at Hoffmann–La Roche; became a widely used anxiolytic and a top-selling drug during the 1970s Fentanyl (1-12; 1968): a ␮-opioid receptor agonist analgesic; synthesized by Paul Janssen; about two orders of magnitude more potent than morphine but of shorter duration of action Propranolol (1-13; 1968): an antihypertensive, antianginal, and antiarrhythmic ␤-blocker; developed by James W. Black at Imperial Chemical Industries, UK from the earlier ␤-adrenergic antagonists dichloroisoprenaline and pronethalol Cimetidine (1-14; 1979): an antiulcerative histamine H2 -receptor antagonist; resulting from the work of James W. Black, C. Robin Ganellin, and others at Smith, Kline and French Cyclosporine (cyclosporin A, 1-15; 1983): a fungal metabolite; isolated at Sandoz while screening for antibiotics; revolutionalized organ transplantation when it turned out to be a potent immunosuppressant capable of preventing rejection Lovastatin (1-16; 1987): a fungal metabolite and the first of the statin class of drugs (HMG-CoA reductase inhibitor); discovered by Akira Endo and Masao Kuroda; received approval for the treatment of high cholesterol levels (hypercholesterolemia) Fluoxetine (1-17; 1987): a widely used specific serotonin reuptake inhibitor type of antidepressant; discovered by David Wong, Jong-Sir Horng, and others at Eli Lilly

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INTRODUCTION

r Sildenafil (1-18; 1998): a cGMP-specific phosphodiesterase type 5 inhibitor; developed at Pfizer originally for use in hypertension and angina pectoris, but now in wide use for erectile dysfunction r Rituximab (1-19; 1997): a chimeric monoclonal antibody against CD20 (a B-cell marker); used in the treatment of many lymphomas and leukemias, in transplant rejection, and for some autoimmune disorders; one of the first successful biotechnology drugs These structures, all shown in Figure 1-1, obviously represent a somewhat subjective selection. Nevertheless, as the result of these developments, many previously deadly infectious diseases, such as cholera, diphtheria, measles, pertussis, plague, scarlet fever, smallpox, tuberculosis, and typhoid fever, are now curable or avoidable. Even if there are still many serious diseases that represent a therapeutic challenge (e.g., Alzheimer’s disease, cancer, influenza, multiple sclerosis, Parkinson’s disease), many others were alleviated and are now manageable for the long term (e.g., asthma, diabetes mellitus, schizophrenia). The mortality associated with syphilis and other sexually transmittable diseases has also been eliminated and even AIDS has become a disease manageable for the long term. All this progress, achieved mostly within the last 100 years, is especially astonishing if we look at it in the light of W. C. Bowman’s comment that “generally speaking, until really quite recently—well into the 20th century in fact—treatment by most available medicines was at best only marginally beneficial and at worst positively harmful” [7]. For a long period of human history, Voltaire (Franc¸ois-Marie Arouet, 1694–1778) was probably right when he noted that “doctors are men who prescribe medicines of which they know little, to cure diseases of which they know less, in human beings of whom they know nothing.” For example, substances that at some point have been used by doctors to treat illnesses include, among many others: snake skin, spider’s web, crocodile dung, frog sperm, and eunuch fat, not to mention mercury and the sexual organs of a variety of animals. For a short period of time during the early twentieth century, Bayer, one of the earliest pharmaceutical companies, was proudly marketing aspirin (1-2) (acetylsalicylic acid—the acetylated derivative of salicylic acid that causes less digestive upset than pure salicylic acid, its active ingredient) together with diacetylmorphine synthesized on the basis of somewhat similar considerations as a safe alternative to morphine (1-1) and even coined the name heroin for it (see Figure 6–4) [6]. The same heroin, of course, is now well recognized as one of our most addictive and socially harmful substances [8], and Bayer quickly stopped marketing it when the problems became obvious. Compared to most of the twentieth century, during the last few decades, progress in identifying true breakthrough drugs may have slowed for a number of possible reasons, which are discussed briefly later, such as increasing focus on safety and regulation, the increasing difficulty of finding new effective targets (i.e., the possibility that all the “low-hanging fruit” has already been picked), the need to outperform all existing drugs (many of which are highly effective), the pursuit of speculative unproven targets, the inherent inefficiency of the very large organizations that are

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THE CHALLENGE OF NEW DRUG DISCOVERY

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left in the pharmaceutical industry, and others ([9,10] and references therein). Nevertheless, new drug launches continue to contribute significantly to improving health care by increasing the quality of life as well as longevity. Life expectancy at birth has been rising continuously in both developed industrialized nations and in less developed regions (Figure 1-2A), due to increasing access to medication and to the introduction of new therapeutic agents. Obviously, it is difficult to estimate exactly how much newly introduced drugs, called new chemical entities, contribute to the continuous worldwide increase in average life expectancy, but according to an estimate on the basis of a complex algorithm, this contribution is close to about half of the total increase seen even since the late 1980s, despite no real lifesaving medical breakthroughs discovered since then (Figure 1-2B) [11]. Even if drugs sometimes seem very expensive, they can be quite cost-effective. An example quoted in a recent book on drug discovery is illustrative [12]: While still a new, proprietary compound, the cost per unit weight of omeprazole was around $200,000 a pound, roughly 500 times more than the corresponding cost of $400 a pound of an F-18 Hornet aircraft, not exactly a cheap technology itself. Even if the cumulative sales of this heartburn medicine were about $40 billion, a large cost at first sight, the use of this drug was estimated to result ultimately in savings to society of about $85 billion, because its use reduced by 75% the gastric surgeries resulting from gastric ulcer complications.

1.2

THE CHALLENGE OF NEW DRUG DISCOVERY

Unfortunately, the discovery and development of a new chemical entity (NCE) that can reach the market as an effective new drug is a long, arduous, and expensive process. The odds of finding a new compound with the right combination of activity, selectivity, stability, and safety are very unfavorable, especially if one considers that the possible (or “allowable”) chemical space is incomprehensibly large [13]. Whereas the simplest living organisms can function with just a few hundreds of molecules (with less than 100 types accounting for nearly the entire molecular pool), and even human bodies might not contain more than a few thousand different types of small molecules at any given time [13], the chemically possible molecular structures represent a very large number. Even if we restrict ourselves to stable and reasonably small compounds (molecular mass 1) or negative (nH < 1) cooperativity: E = E max

[L]n H + K dn H

[L]n H

(2.7)

This function introduced by A. V. Hill early in the twentieth century [36,37] provides a versatile mathematical function and is often used in pharmacological [38] or other applications (e.g., [39]). The Clark equation [eq. (2.6)] as well as the analogous Michaelis–Menten equation [see eq. (2.22)] [40] used in enzyme kinetics represent a special case (nH = 1) of the Hill equation. Competitive and Noncompetitive Antagonism In the presence of an antagonist, the response produced by the agonist is diminished. In general, various antagonism

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100 90 80

Response (%)

70 60

Agonist alone

50

w comp.inhib.1 w comp.inhib.2

40

w noncomp.inhib.1 w noncomp.inhib.2

30 20 10

pIC50

0 -11

-10

-9

-8 -7 Log Concentra
on

-6

-5

FIGURE 2-3. Typical agonist response described by the sigmoid curve corresponding to the Clark equation [eq. (2.6)] in a semilogarithmic plot as used here (solid line) and the effect of (pure) competitive (orthosteric) and noncompetive (allosteric) agonists (dashed and dashed–dotted lines, respectively).

mechanisms are possible; among those involving the receptor, competitive (orthosteric) and noncompetitive (allosteric) are the two main mechanisms of interest. Competitive antagonists bind at (compete for) the same site as the agonist, which can be envisioned schematically as having an inhibitor–receptor complex (IR), whose formation competes with that of the ligand–receptor complex [LR of eq. (2.1)]. Such antagonists produce an apparent decrease in the affinity of the agonist (right shift; Figure 2-3), characterized by the Gaddum equation, which is similar to the Clark equation but with an apparent increased Kd (depending on the concentration of the inhibitor present [I] and its binding constant Ki ): E = E max

[L] [L] + K d (1 + [I]/K i )

(2.8)

Contrary to these, noncompetitive antagonists do not bind at (compete for) the same site as the agonists. They bind at some different (allosteric) site and modulate the effect of agonist via this binding. Consequently, their effect is not surmountable (i.e., it cannot be overcome by increasing the concentration of the agonist), and they do not cause a right shift but, instead, an apparent diminished maximum response (Figure 2-3). A possible simple model is to assume that the inhibitor I bounds to a separate site of the receptor so that in addition to the ligand–receptor complex (LR) of eq. (2.1), IR and ILR complexes can also form, which, however, are nonfunctional and do not produce the (desired) effect E. The resulting equation for the effect indeed

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reveals a diminished maximum response as a function of the inhibitor concentration [I], and this is also illustrated in Figure 2-3: E = E max

[L] 1 [L] + K d (1 + [I]/K i )

(2.9)

As a final note on this subject, we should mention the Cheng–Prusoff equation [41] to illustrate a connection between the binding constants Kd used in these quantitative models [eqs. (2.6 to 2.9)] and other typical efficacy measures, such as those mentioned earlier (EC50 , IC50 , etc.). The Cheng-Prusoff equation applies to the case of the often-employed competitive binding assay, in which displacement of a known radioor otherwise labeled ligand (i.e., L∗ with a known K d∗ ) is used to measure the (inhibitory) binding constant Ki of an unlabeled compound (I), by measuring IC50 , the [I] concentration causing 50% displacement of the labeled ligand L∗ . From the measured IC50 , the corresponding binding constant of the inhibitor is obtained via the Cheng–Prusoff equation: Ki =

IC50 1 + [L∗ ]/K d∗

(2.10)

In other words, the IC50 measured does not correspond exactly to Ki , as it is also influenced by the particular experimental conditions used ([L∗ ], K d∗ ). However, in most cases it is a good estimate, as typical assay conditions require the use of ligand concentrations in the range of its binding constant (K d∗ ) so that the [L∗ ]/K d∗ ratio in the denominator is a relatively small number. Obviously, if sufficiently low ligand concentrations are used in the assay ([L∗ ]  K d∗ ), the IC50 obtained corresponds directly with the value of Ki . Ligand Efficacy (and Ligand Affinity) Receptor occupancy theory, as discussed until now, is the simplest approximation—it cannot account for partial agonists, desensitization, and other phenomena; hence, in many cases, more complex models are needed. In addition to occupancy resulting from binding (affinity), some measure is needed for the ability of the ligand to induce a response at the receptor. The simplest useful model is the del Castillo–Katz model, which corresponds to a minimal “twostate theory” in which occupied and active receptor states no longer fully correspond [cf. eq. (2.1)]: occupied

vacant

[L] + [R]

Kd inactive

[LR]

Kε

[LR*]

Effect

(2.11)

active

This assumption results in an effect function somewhat similar to the simple Clark equation [eq. (2.6)], but one that also allows for both affinity (via Kd as discussed

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above) and efficacy (via K ε , the equilibrium constant for the activation of receptor with a bound ligand) [1,34,35]: E = E max

K ε [L] (1 + K ε ) [L] + K d

(2.12)

Most current quantitative pharmacological models assume a version along these lines, with many more complex variations to allow for constitutive activity and other effects [1,34]. 2.2

PHARMACOKINETIC PHASE: ADME

As noted earlier, before even reaching their intended target, drug molecules have to undergo and/or survive a series of sequential processes involving, among others, absorption, distribution, metabolism, and excretion (ADME) (Figure 2-1). Their ability to do so is an important element determining the (therapeutic) effect they can ultimately produce. Hence, the physicochemical properties influencing their ADME behavior play an important overall role, irrespective of the structural elements needed to produce a response in the pharmacodynamic phase discussed above. 2.2.1

Drug Absorption and Distribution

Following absorption, drugs undergo transport by convection (e.g., by flow in the bloodstream) and by diffusion (for the remaining shorter distances). To a good extent, the body can be considered as a series of interconnected well-stirred compartments in which the drug concentration is uniform, that are separated by diffusion barriers. The distribution of a particular drug (i.e., where and for how long it will be present) is determined by its ability to move between these compartments, which generally involves the penetration of nonaqueous diffusion barriers. Therefore, to reach their target, in general, drugs have to be able to traverse cellular barriers, including the gastrointestinal mucosa, the blood–brain barrier, the cell membrane (for intracellular targets), and others. Crossing of the cell membranes is usually done via passive diffusion (either directly through the lipid or via the aqueous pores that might be present) or by some carrier-mediated transfer. For most drugs, diffusion through the lipid is the most important mechanism, and their ability to do so is determined primarily by their lipid solubility (lipophilicity). Lipophilicity and Its Role Lipophilicity (“fat loving”) as a property characterizes the ability of a chemical compound to dissolve in fats, oils, lipids, and nonpolar solvents in general, and it is often used interchangeably with hydrophobicity (“water fearing”) as they indeed tend to correlate closely for most organic compounds. As “like dissolves like” is a useful rule of thumb, polar compounds tend to be water soluble (i.e., hydrophilic), whereas nonpolar compounds tend to be lipid soluble (i.e., lipophilic). Polar compounds are unable to cross the cell membrane via passive diffusion; hence, nonpolar (lipophilic) compounds tend to be much better at reaching the

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body compartment where their target is located. Accordingly, the log n-octanol/water partition coefficient (log Po/w ), a widely used measure of lipophilicity and hydrophobicity, represents one of the most informative physicochemical parameters available to medicinal or environmental chemists. It is defined (for dilute solutions) as the molar concentration ratio of a single species (D) between two phases at equilibrium: Pi/j =

[D]i [D] j

(2.13)

Here we denote the partition coefficient as P, a notation usually favored by medicinal and pharmaceutical chemists. Environmental and toxicological chemists tend to prefer log K (log K o/w for octanol/water), a reminder of the fact that this is, after all, an equilibrium constant. Usually, logarithms (log P) are employed because of the wide range to be covered, often close to 8 to 10 orders of magnitude, and because of the theoretical justification of linear free-energy relationships resulting from the fact that, in thermodynamics, P(K) can be considered a free-energy function [in the sense of eq. (2.3)]. Selection of the octanol/water system as the reference is usually rationalized as modeling the partition between aqueous and biophases [42,43]. Octanol, with a polar head and a flexible, nonpolar tail, has hydrogen-bonding capabilities and amphiphilicity characteristics similar to those of the phospholipids and proteins found in biological membranes. Since life as we know it is water based, the physiological roles that molecules can play are closely related to their ability to solvate into or partition from water in general; hence, lipophilicity and related physicochemical properties such as partition coefficients clearly play important roles. Interest in this partition coefficient (log Po/w ) began around 1960, due to impressive work by Hansch and co-workers [42,44–47]. However, the concept had been developed and the first physicochemical studies had been performed almost 100 years earlier in the 1870s by Berthelot [48–50] and later by Nernst [51]. A historical review of many of these early concepts back to the alchemist experience (corpora non agunt nisi soluta) can be found in the work of Arrhenius [52]. Further details on lipophilicity and log Po/w have been reviewed earlier [53] and are discussed here later in the context of retrometabolic drug design and brain targeting. Ionization and Its Role The overwhelming majority of drugs are ionizable; they are either weak acids or weak bases that are in various states of ionization, depending on the pH of the surrounding media. Most drugs are basic (about 75%), some are acidic (about 20%), and only very few are nonionizable (ca. 5%) [54]. The reason for this is that drug transport has to be a compromise between the increased (aqueous) solubility of the ionized form and the increased permeability of the nonionized (nonpolar) form through the lipid bilayer of cell membranes (for a weak acid, the neutral form is protonated; for a weak base, the neutral form is unprotonated). The ionization ability is described quantitatively and the amount of ionized species is calculated via the acid constant Ka and the Henderson–Hasselbalch equation. For an acid (proton donor), the equilibrium can be described as a reversible bimolecular reaction [similar to eq. (2.1)], HA
A– + H+ , so that the acidity

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constant Ka is defined in a manner similar to the binding constant Kd [eq. (2.2)]:  −  + H A (2.14) Ka = [AH] From here, with the usual definition of pH as the negative logarithm of hydrogenion concentration, pH = −log [H+ ] (= −log[H3 O+ ] in aqueous media), the Henderson–Hasselbalch equation giving the ratio of unprotonated to protonated form for an acid is easily obtained: log

[AH] = pK a − pH [A− ]

(2.15)

This means that, for example, warfarin, a weak acid (pKa 5.0), is mostly ionized (unprotonated) at physiologic pH (pH 7.4); the ratio of protonated to unprotonated form is approximately 1/251 (105.0–7.4 ). Similar but slightly different equations apply for bases. For a base (proton acceptor), the equilibrium can be described as, HB+
B + + H , so that the acidity constant Ka is defined as:   [B] H+ Ka =  + (2.16) BH and the corresponding Henderson–Hasselbalch equation giving the ratio of unprotonated to protonated form for a base becomes  + BH = pK a − pH log (2.17) [B] Because polar compounds are usually unable to cross cell membranes, in general only the uncharged species can diffuse across lipid membranes in significant amounts. Therefore, ionization gives rise to a phenomenon known as pH partition: Weak acids tend to accumulate in more basic compartments (relatively high pH, where they are more ionized), whereas weak bases tend to accumulate in more acidic compartments (relatively low pH, where they are more ionized). As another (related) consequence, acids are better absorbed from an acidic stomach where the proportion of the nonionized form capable of crossing the membrane barrier is higher; bases are better absorbed from the more basic intestines (pH 6.0 to 8.3). 2.2.2

Drug Metabolism and Excretion

Drug Elimination Mechanisms Once absorbed, drugs are subject to elimination via two main processes, metabolism and excretions (Figure 2-4). Metabolism involves enzymatic conversion into a different chemical entity within the body; excretion is the elimination of the unchanged drug (or its formed metabolites). Most drugs are cleared from the body via the kidneys into the urine—either unchanged (if sufficiently polar) or as a polar metabolite. Some drugs are excreted via the liver into the bile,

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CYP

Clearance mechanisms

Metabolism

Renal

Bile

UGT

Esterases

FMO

NAT

CYP3A

CYP2C9

CYP2C19

CYP2D6

CYP1A2

CYP2E1

CYP2B6

CYP1A1

UGT2B7

UGT1A4

UGT1A1

UGT1A8

UGT1A10

UGT2B4

UGT1A3

UGT1A6

UGT

Enzymes contribu
ng to clearance

CYP

23

MAO

FIGURE 2-4. Clearance mechanism of the top 200 drugs prescribed in the United States in 2002 and the enzymes involved in their metabolism (data after [56]). Individual pie charts are (counterclockwise from top left): listed clearance mechanisms (from http://www.rxlist.com); listed enzymes contributing to clearance for metabolized drugs; proportion of UDP–glucuronosyltransferase (UGT) substrates in the top 200 metabolized by each member of that subfamily listed; and the proportion of cytochrome P450 (CYP) substrates in the top 200 metabolized by each member of that subfamily listed. (See insert for color representation of the figure.)

and some volatile compounds are also eliminated via the lungs. Most drugs cross the glomerular filter freely (unless they are highly bound to plasma proteins); however, lipophilic compounds are passively reabsorbed by diffusion across the tubule, so that they are not excreted efficiently in the urine. Because of the above-mentioned pH partition, weak acids tend to be excreted more rapidly in alkaline urine, and weak bases tend to be excreted more rapidly in acidic urine. It is also important to remember that renal excretion can be considerably impaired in elderly persons and patients with renal disease, and this can result in severe toxicity for several important drugs that are removed primarily by this route. Metabolic Biotransformation Metabolism is a result of the complex detoxification mechanisms evolved in general for xenobiotics. Not surprisingly, most critical metabolic pathways are mediated by oxygenases, since as A. Albert has noted: “An

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organism’s normal reaction to a foreign substance is to burn it up as food” [55]. The cytochrome P450 (CYP) superfamily is a large and diverse group of enzymes that is involved in the oxidation of many organic substances, including most drugs (Figure 2-4). For drugs cleared via metabolism, about 75% are metabolized by CYP enzymes, primarily CYP3A (46% of all CYP-mediated metabolism), CYP2C9 (16%), CYP2C19 (12%), CYP2D6 (12%), and CYP1A (9%) [56,57]. The cytochrome P450 name for these enzymes comes from their cellular location (cyto) and spectrophotometric characteristics (chrome 450, as they tend to absorb light at wavelengths near 450 nm when the reduced iron from their heme cofactor forms an adduct with carbon monoxide). As just discussed, most drugs have to be able to cross through biological membranes to exert their action; therefore, they tend to be quite lipophilic, which, however, also makes them not particularly good substrates to renal excretion since they can be reabsorbed from the renal tubules after glomerular filtration. Metabolites formed by biotransformations are in general less lipophilic (more hydrophilic) than their parent structure, to allow for their excretion by the kidneys. In general agreement with all these facts, metabolism tends to be more important for lipid-soluble drugs than for polar drugs. Most biotransformation reactions take place in the liver (hepatic metabolism), but biotransformation can also occur in the intestinal mucosa, lungs, kidneys, skin, placenta, and plasma. First-pass metabolism (i.e., presystemic metabolism in the liver or gut wall before the drug reaches the systemic circulation) can reduce the bioavailability of certain drugs considerably when they are administered by mouth. Metabolic processes usually inactivate a drug D, but in some cases, active or toxic metabolites (Mk , I*n ; Figure 2-1) can be generated, as discussed in detail later. It is also important to remember that since these various metabolites can be present in the body simultaneously with the original drug (Figure 2-1), sometimes quite complex situations are created and the overall activity (as well as toxicity) observed is that of all these compounds together [see eq. (2.25)].

Drug Metabolism: Phase I and II Reactions Metabolic processes are classified as involving phase I reactions, which are catabolic processes designed to break down the molecules and release energy (e.g., oxidation, reduction, hydrolysis) and phase II reactions, which are anabolic processes designed to build up molecules from smaller units (e.g., conjugation). Phase I reactions, which involve oxidation, reduction, and hydrolysis, usually form more chemically reactive products, which can be pharmacologically active, toxic, or carcinogenic. They often involve a monooxygenase system in which cytochrome P450 plays a key role; in fact, about 90% of phase I metabolism is mediated by these enzymes [58]. Most typical pathways are as follows: oxidations, such as aliphatic or aromatic hydroxylation, epoxidation, N-oxidations, S-oxidation, oxidative dealkylations (N-, O-, or S-dealkylations), and others; reductions, such as carbonyl, nitro, or azo reductions; and hydrolyses, such as ester or amide hydrolysis (Figure 2-5). Phase II reactions involve conjugation (e.g., glucuronidation) of a reactive group, which in many cases has been inserted during a preceding phase I reaction, and usually lead to inactive and polar products that

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FIGURE 2-5. Some representative phase I metabolic reactions occurring most commonly for typical drug structures.

are readily excreted. Typical phase II reactions include glucuronic acid conjugation (i.e., O-, N-, or S-glucuronidations), acetyl conjugation (acetylation), glutathione conjugation, water conjugation, and others. Some conjugated products are excreted via the bile, are reactivated in the intestine, and are then reabsorbed (“enterohepatic recirculation”). A graphical summary of the clearance mechanism of top 200 drugs prescribed in the United States and the enzymes involved in their metabolism [56] is shown in Figure 2-4. Obviously, this illustration focuses only on the main elimination and metabolic pathways; drugs may be metabolized by two or more of these pathways. Notably, two pathways, CYP3A4/5 and UGT, are involved in the metabolism of more than 75% of drugs in use [59]. Several P450 enzymes have relatively limited capacity, and many of them are subject to inhibition or induction by different substances. This can give rise to severe drug–drug interactions where hepatic drug metabolism is either accelerated or inhibited altering the rate of metabolism significantly and hence the amount of drug circulating.

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FIGURE 2-6. Typical concentration–time PK profile for an orally administered drug following first-order absorption/first-order elimination kinetics (heavy line; described by a typical two-exponential function) together with a corresponding profile following i.v. administration (light line).

2.2.3

Basic Pharmacokinetic Concepts

All these ADME processes result in continuously changing drug concentrations in the various compartments of the body; the quantitative characterization of this is the subject of pharmacokinetics (PK). Accordingly, pharmacokinetics is concerned with the description of the time course of drug concentration in the body (what the body does to the drug) in contrast to pharmacodynamics (PD), which, as discussed above, is concerned with the concentration–effect relationship (what the drug does to the body). PK is critical to the characterization and understanding of the time course of drug action and when calculating dosing regimens. During the drug design and development phase, PK characterization is also important in selecting the most suitable drug candidate and in optimizing its formulation. There are two fundamental physiologically based PK parameters—clearance and volume of distribution—and another frequently used important one, the elimination half-life, t1/2 (or the closely related elimination rate constant, kel ). A typical plasma concentration–time profile obtained for an orally administered drug is shown in Figure 2-6 together with commonly used PK characteristics such as the maximum concentration Cmax , the time to maximum concentration, tmax , and the area under the concentration curve, AUC0-t . AUC is a measure of drug exposure and represents the integral of the concentration vs. time, AUC = C(t)dt [usually estimated from experimental data using the trapezoidal rule, AUC0-t = (Ci+1 + Ci ) (ti+1 – ti )/2].

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Clearance and Volume of Distribution The clearance, CL, is defined as the volume of blood (irreversibly) cleared of drug per unit time; hence, it is measured in units of volume per time (e.g., L/h, mL/min). In general, the drug is rarely fully removed from the blood as it passes through the liver; therefore, for example, a CL rate of 60 L/h in humans, where the typical liver blood flow is QL = 90 L/h, means that twothirds (60/90) of the drug entering the liver in the blood is removed. CL determines the rate of drug elimination at a given concentration C: rate of elimination = CL × C. Therefore, it determines the maintenance dose at steady state (where elimination equals the input rate): maintenance dose rate [mg/h] = CL [L/h] × Css [mg/L] (a representative set of units of measurements is included in brackets for illustration only). It also determines AUC for a given dose D: AUC [mg/L · h] = D [mg] / CL [L/h]. The volume of distribution, Vd , relates the total amount of drug in the body to its concentration in plasma: Vd = D/C. Vd is a useful concept, but it is not a “real” volume because drugs are not distributed uniformly within the body—they can partition away from the plasma to other tissues. Consequently, for some drugs, Vd can be much higher than the total volume of plasma (ca. 3 L in humans) or even the total volume of body (e.g., Vd is 150 L for quinidine or 2100 L for imipramine). The volume of distribution determines the loading dose needed to reach a target concentration, Dloading [mg] = Vd [L] × Ctarget [mg/L]. Elimination Half-Life As indicated by its name, the half-life denotes the time required for the plasma concentration (or drug amount) to decrease by half. The concept of a half-life is valid only for exponential elimination (i.e., cases where the concentration decreases as an exponential function of the time): C = C0 e−kel t

(2.18)

This corresponds to a first-order elimination, cases where the rate of elimination is proportional to the amount present; that is, the rate of elimination = −dC/dt = kel C (kel being the elimination constant). For exponential decay, the half-life is indeed constant: at any concentration, the time required for halving is the same. The value of the half-life, t1/2 , is related directly to the elimination constant and can be obtained easily by calculating the time needed to reach the half of C0 by using C(t1/2 ) = C0 e−kel t1/2 = C0 /2, which results in t1/2 =

ln(2) 0.693 = kel kel

(2.19)

The three PK parameters discussed earlier (CL, Vd , and t1/2 ), are directly interconnected via a relationship that can be obtained easily from the definition of the rate of elimination: CL = kel Vd =

0.693 Vd t1/2

(2.20)

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The elimination half-life is a major determinant of the duration of action after a single dose because, for most drugs, the plasma concentration has to stay within the effective range to produce the desired effect. It is also a major determinant of the dosing frequency required to avoid large fluctuations in plasma concentration; as a rule of thumb, the interval between doses cannot be much larger than the half-life. Finally, t1/2 also determines the time required to reach steady state with chronic dosing since, as long as linear PK applies, it takes four to five half-lives to reach steady state, irrespective of the dose, the dosing frequency, or other factors. In most cases [except intravenous (i.v.) administration], drug absorption is also gradual. Resulting plasma concentrations are typically of the shape illustrated in Figure 2-6, which corresponds to a case of first-order absorption (kabs ) and first-order elimination (kel ) described by a two-exponential function: C = C0

 kabs  −kel t e − e−kabs t kabs − kel

(2.21)

As mentioned, the concept of elimination half-life only applies for first-order eliminations, where the rate of drug elimination is proportional to the amount of drug present, dC/dt = –kel C. As the amount of drug is usually low, this is the case in the majority of cases; however, in a few cases, drug concentration decreases linearly (zero-order) and not exponentially (first-order), meaning that the elimination rate is constant and independent of the amount of drug present. Notable examples are ethanol, phenytoin, or salicylate. Typically, this occurs due to saturation of the elimination mechanism (e.g., saturation of the main metabolizing enzyme). Most enzymes follow a Michaelis–Menten kinetics [40,60], with a rate that is determined by the concentration of the substrate (C) as well as the maximum velocity V max and the characteristic Michaelis constant K MM of the enzyme via an equation that has a form similar to the Clark [eq. (2.6)] and Hill [eq. (2.7)] equations: v = Vmax

C C + K MM

(2.22)

At low concentrations (C  K MM ), this corresponds to a first-order case, where the rate is proportional to the concentration (i.e., first power of concentration), −dC/dt = v = (Vmax /K MM )C = kel C 1 . At high concentrations, however (C  K MM ), this corresponds to a zero-order case, where the rate is constant and equal to the maximum velocity, as the enzyme is fully saturated, −dC/dt = v = Vmax = k  C 0 . Under such conditions, the amount of drug eliminated per unit time is constant, and the concept of half-life cannot be applied (see also the discussion in section 5.1.5). Bioavailability A final pharmacokinetic notion that should be discussed briefly is bioavailability (F). Because of their convenience and suitability for widespread use, oral drugs are the ultimate target for almost any drug discovery or development program. However, obtaining oral drugs often represents a considerable challenge, partly because of formulation issues (i.e., solubility, stability, etc.) and partly because

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sometimes only a small fraction of an orally administered drug actually reaches the systemic circulation, as it might not be absorbed well or it might undergo significant first-pass metabolism (Figure 2-1). Bioavailability (F) denotes the fraction of an orally administered dose that actually reaches the systemic circulation as intact drug, and F = fg × fH , fg being the fraction absorbed (from intestine into portal circulation) and fH the fraction escaping first-pass clearance (metabolism) (i.e., the fraction not removed by the liver during the first passage in the portal blood through the liver to the systemic circulation). Because the area under the concentration curve (AUC) is determined by the dose and the clearance, AUC = D/CL, and the clearance is essentially the same regardless of the route of administration, the ratio of the AUCs following administration of the same dose orally (p.o.) and intravenously (i.v.) can be used to assess the bioavailability (Figure 2-6): F=

2.3

AUCp.o. AUCi.v.

(2.23)

STRUCTURAL REQUIREMENTS: KEEPING IT “DRUG-LIKE”

During the last decade or so, drug-likeness has become a desirable strategic target [54,61,62]. This concept became of particular interest during the 1990s with the development of high-speed screening and chemistry methods that expanded the chemical diversity of the screened collections, and during this process with time it became clear that an unacceptably large portion of the candidates were not making it to the final stages. However, one has to remember that drug-likeness is a fuzzy term; there is no clear delineation between drug and nondrug structures, and there probably never will be [63]. 2.3.1

The Drug-Like Chemical Space

At best, drug-likeness can be used as a design guideline to keep the small-molecule drug candidates within a chemical space that, on the basis of existing experience, seems to offer an increased chance of surviving clinical trials and becoming a drug. When dealing with novel structures intended for pharmaceutical use, it is certainly useful to remember that 70% of existing drugs have zero to two hydrogen-bond donors, two to nine hydrogen-bond acceptors, two to eight rotatable bonds, and one to four rings [64]. Interestingly, analyses of commercially available drugs found repeatedly that the diversity of structures and shapes in the set of known drugs is surprisingly low [65–68]. For example, bioactive molecules contain only a relatively limited number of unique ring types [67], and an analysis of the basic ring-structure framework of existing drugs revealed surprisingly low diversity; half of the drugs have shapes described by only 32 of the 1179 possible frameworks [65] (Figure 2-7). Even the diversity that side chains provide to drug molecules is quite low, the average number of side chains per molecule being four and the average number of heavy atoms per side chain being two [66]. In fact, this seems to be true for existing organic

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MECHANISM OF DRUG ACTION: BASIC CONCEPTS N

decreasing frequency

N N

O

N

N

N

N

N N

S

N

N

N

N

N

N

O

decreasing frequency

decreasing frequency

O

N

O O

S N N

H N

S N

FIGURE 2-7. Some of the most common molecular frameworks found for compounds in the Comprehensive Medicinal Chemistry (CMC) database (MDL Information Systems Inc., San Leandro, CA) shown in decreasing frequency from left to right and top to bottom (as classified by topological torsions in [65]).

chemical compounds in general: An analysis of the molecular framework data from more than 24 million organic compounds in the CAS Registry found that half can be described by only 143 framework shapes [69]. The framework distribution conformed well to a power law [i.e., the probability of occurrence decreased following a power function as p(x) = ␣·x−␯ ; here ␯ = 2.07], suggesting that exploration of the chemical space is governed by a “rich get richer” type of process whereby the more often a framework has been used in the past, the more likely chemists are to use it to make a new compound [69]. Nevertheless, it is highly unlikely that property-based classifications or other relatively simplistic attempts (e.g., such as the topological, neighborhood descriptor– based prediction of activity spectra [70]) will ever be able to rigorously distinguish a drug-like space from a non-drug-like space [71]. It is worth not forgetting that even structurally similar compounds may have very different biological activities [72]. However, while searching for structures likely to succeed as drugs, it is at least important to remember that one should, at a minimum, avoid compounds that are known promiscuous inhibitors [73,74], frequent hitters [75], or contain chemically reactive functional groups such as protein-reactive electrophilic false positives as well as chelator and polyionic “warheads” (see [76] for a brief structural review). The existence of many promiscuous inhibitor structures, which showed up frequently as hits in high-throughput in vitro screenings but proved worthless in later in vivo testing and might simply act through a common mechanism as aggregate-forming

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nonspecific inhibitors, illustrates well the possibility of many misleading pathways [73]. For several cases, polymolecular conglomeration [77,78] and aggregation [73, 79] have been suggested as possible mechanisms. Interestingly, overall promiscuity seems to be controlled predominantly by lipophilicity and ionization state; bases and quaternary bases being notably more promiscuous than acids or neutral compounds, but promiscuity correlates positively with lipophilicity (log Po/w ) in all ionization classes [80]. With regard to the drug-like chemical space, it is also useful to remember that certain structural scaffolds are known to be particularly effective for drug design purposes; such building blocks (e.g., benzodiazepines) have been termed privileged structures [81,82], and, of course, many of them can be recognized among the most common drug frameworks (Figure 2-7). On the other hand, it is also important to avoid structural components that are known to cause problems (e.g., functional groups such as aldehydes, hydrazines, sulfonylureas, nitroaromatics, and many others that are reactive or result in toxic metabolites), and most drug design programs now routinely use structural alert notifications [83]. Various typically computational filters are now used routinely in drug discovery and development programs to eliminate as many candidates as possible that are ultimately unlikely to succeed—if possible, even before chemical synthesis and certainly before the start of detailed in vitro/in vivo testing. This is, in fact, the popular “fail early, fail cheap” strategy taken to its extreme, and it can provide significant savings in time and expense. Such preselections are certainly useful and should serve as a “reality check” in any drug discovery program. Nevertheless, they should not be used as simplistic “hard” filters in managerial-type decision making (“go” vs. “no-go”), which is their most frequent current use, but only as “soft” bias in a scientific selection process. A number of existing top-selling drugs would not have made it through many drug-likeness filters [84]. Furthermore, paradigm changes or shifts may always occur, novel structural motifs may not fit existing rules, drug development techniques are evolving, and thinking outside the box should not be confined to hard filtering rules. Because in silico and even in vitro models are prone to large errors and are still not very predictive of in vivo performance, stringent exclusion principles should be combined with liberal promotion principles to identify better overall drugs [85]. 2.3.2

Oral Drugs: The Challenge of Bioavailability

As mentioned, oral drugs are the ultimate goal of essentially all drug development projects because of their convenient, patient-friendly administration, which makes them easy to market for almost any population. Hence, many drug-likeness filters are in fact filters for selecting oral drug candidates. A set of heuristic rules known as the rule of five or Lipinski’s rule of five [86], which were derived from an analysis of the properties of marketed drugs, has now become in almost standard use to avoid permeability and solubility problems and to maximize the chances of surviving development for oral drug candidates. The rule of five requires structures with molecular mass below 500, fewer than five hydrogen-bond donors, fewer than 10 hydrogen-bond acceptors, and a calculated CLOGP under 5. In a later, alternative approach [87] it was suggested that since molecular mass is a surrogate for many

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properties, large molecular mass as such is not the main limiting factor, but large molecular flexibility. Hence, adequate oral bioavailability (in rats) seemed to require fewer than 12 hydrogen-bond donors and acceptors (or low polar surface area, e.g., < 140 Å, as a possible descriptor) and fewer than 10 rotatable bonds [87]. Furthermore, as medicinal chemists tend to tinker with their original lead structures to improve their properties or to avoid continuously surfacing development problems, comparisons of commercial drugs and their corresponding leads indicated that the original lead structures tend to have lower mass (MW), lower lipophilicity (CLOGP), and fewer hydrogen-bond acceptors than those of the final drugs [88,89]. On this basis, searches for oral drugs are likely to end successfully only if starting from even more restricted libraries (e.g., 100 < MW < 300, 1 < CLOGP < 3). This idea is also supported by a different analysis, which looked at drug candidates in various stages of development (preclinical, phase I, phase II, phase III, and launched drugs) and found that the percent of compounds that do not satisfy these rules and have MW > 500, CLOGP > 5, and rotatable bonds > 10 clearly decreases as the development stage advances [90]. It is also clearly recognized by now that almost all ADME and toxicological parameters tend to deteriorate with increasing molecular size and/or lipophilicity (log P) [91]. Even after 10 years of use of the rule of five doctrine, the physical properties of molecules that were being synthesized in leading drug discovery companies were still significantly different from those of recently discovered oral drugs and compounds in clinical development: for example, they were, on average, much more lipophilic [80]. The fact that lipophilicity changed less over time than any other property in oral drugs launched is a clear indication of its importance [80], and the observation that, over time, lipophilicity remained essentially unchanged in marketed drugs is even more remarkable because it stayed unchanged despite the increase in candidates considered for drug discovery [92]. Predicting what makes a good oral drug is also made particularly challenging by the fact that absolute oral bioavailability data in experimental animals (rodents, dogs, and even primates) are, at best, only mildly predictive for absolute oral bioavailability in humans [93]. Monkeys do appear to be a better predictor of oral bioavailability in humans [94]. Drug absorption is a complex process that depends on many factors and has to be addressed as such [95]. Consequently, there seems to be no simple relationship between lipophilicity (e.g., as measured by the log partition coefficient, log P, of the neutral compound or the log distribution coefficient, log D, of ionizable or permanently charged compounds) and bioavailability [96,97], but acceptable oral bioavailability (and biomembrane permeability) tends to require not too large, not too polar, not too flexible compounds with not too many hydrogen-bonding sites [86,87, 98–101]. Most likely, high solubility and moderate lipophilicity can be considered as the typical characteristics of well-absorbed compounds [102]. Along these lines, the Biopharmaceutics Classification System (BCS) [103], which has a four-group classification system according to high/low aqueous solubility and high/low intestinal permeability, is commonly used to predict the extent of drug absorption during the course of drug development. Increasing lipophilicity often leads to an increased rate of oxidative metabolism by cytochrome P450 and other enzymes [58,104].

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Consequently, in most cases, increasing lipophilicity increases potency and membrane permeability but decreases dissolution and metabolic stability [105]. Not surprisingly, there is a very strong correlation between the intestinal permeability rate and the extent of metabolism; accordingly, the Biopharmaceutics Drug Disposition Classification System (BDDCS) has been proposed as a possible alternative to the BCS, substituting high/low (i.e., extensive/poor) metabolism for high/low permeability [106,107].

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[92] Walters, W. P.; Green, J.; Weiss, J. R.; Murcko, M. A. What do medicinal chemists actually make? A 50-year retrospective. J. Med. Chem., 2011, ePub. [93] Sietsema, W. K. The absolute oral bioavailability of selected drugs. Int. J. Clin. Pharmacol. Ther. Toxicol., 1989, 27, 179–211. [94] Chiou, W. L.; Buehler, P. W. Comparison of oral absorption and bioavailablity of drugs between monkey and human. Pharm. Res., 2002, 19, 868–874. [95] Burton, P. S.; Goodwin, J. T.; Vidmar, T. J.; Amore, B. M. Predicting drug absorption: how nature made it a difficult problem. J. Pharmacol. Exp. Ther., 2002, 303, 889–895. [96] Yoshida, F.; Topliss, J. G. QSAR model for drug human oral bioavailability. J. Med. Chem., 2000, 43, 2575–2585. [97] Raevsky, O. A.; Fetisov, V. I.; Trepalina, E. P.; McFarland, J. W.; Schaper, K.-J. Quantitative estimation of drug absorption in humans for passively transported compounds on the basis of their physico-chemical parameters. Quant. Struct.-Act. Relat., 2000, 19, 366–374. [98] Vieth, M.; Siegel, M. G.; Higgs, R. E.; Watson, I. A.; Robertson, D. H.; Savin, K. A.; Durst, G. L.; Hipskind, P. A. Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem., 2004, 47, 224–232. [99] Martin, Y. C. A bioavailability score. J. Med. Chem., 2005, 48, 3164–3170. [100] Refsgaard, H. H.; Jensen, B. F.; Brockhoff, P. B.; Padkjaer, S. B.; Guldbrandt, M.; Christensen, M. S. In silico prediction of membrane permeability from calculated molecular parameters. J. Med. Chem., 2005, 48, 805–811. [101] Johnson, S. R.; Zheng, W. Recent progress in the computational prediction of aqueous solubility and absorption. AAPS J., 2006, 8, E27–E40 (art. 4). [102] Curatolo, W. Physical chemical properties of oral drug candidates in the discovery and exploratory development settings. Pharm. Sci. Tech. Today, 1998, 1, 387–393. [103] Amidon, G. L.; Lennernas, H.; Shah, V. P.; Crison, J. R. A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability. Pharm. Res., 1995, 12, 413–420. [104] van de Waterbeemd, H.; Smith, D. A.; Beaumont, K.; Walker, D. K. Property-based design: optimization of drug absorption and pharmacokinetics. J. Med. Chem., 2001, 44, 1313–1333. [105] Smith, D.; Schmid, E.; Jones, B. Do drug metabolism and pharmacokinetic departments make any contribution to drug discovery? Clin. Pharmacokinet., 2002, 41, 1005–1019. [106] Wu, C. Y.; Benet, L. Z. Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system. Pharm. Res., 2005, 22, 11–23. [107] Benet, L. Z.; Broccatelli, F.; Oprea, T. I. BDDCS applied to over 900 drugs. AAPS J., 2011, 13, 519–547.

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The Drug Discovery and Development Process

Let us now review briefly the current drug discovery and drug development process in general. A summary of its main phases is presented in Figure 3-1 [1–4]. Obviously, this is an idealized imaginary case of discovering and developing a completely new drug for a new target; some of the early development phases might be different in many cases. For a comprehensive review, we also assume a case of rational drug design, where first the disease is understood at a molecular level and the biological macromolecules involved are identified and fully characterized so that compounds intended to modulate their function in a therapeutically beneficial manner can be designed, prepared, and tested following a logical approach.

3.1 3.1.1

DISCOVERY RESEARCH Prediscovery

For a rational approach, as a first step before any search for a new therapy can be initiated, the disease needs to be understood and its etiology needs to be established: the genes and proteins involved as well as the dysregulation that ultimately cause the pathological conditions need to be identified. Fortunately, during the past 50 years or so, tremendous advances have been made in molecular biology, physiology, biochemistry, genetics, pharmacology, and many related fields. Unfortunately, however, there are still serious gaps in our understanding of most diseases, and often, even what we understand is difficult to translate into something that has, or can have, therapeutic value. Hence, this phase, which can involve many researchers from very different places in academia and in industry as well, may be very unpredictable, with many dead-ends, many small incremental advances over years or even decades, or a few real breakthroughs during a much shorter period. In fact, many of the original mechanisms of action proposed for existing drugs later turned out to be wrong—the drugs were effective just they were acting not (or not just) along the pathway first proposed for their action.

Retrometabolic Drug Design and Targeting, First Edition. Nicholas Bodor and Peter Buchwald.
C 2012 John Wiley & Sons, Inc. Published 2012 by John Wiley & Sons, Inc.

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4-8 2-3 8 - 15 compounds compounds compounds

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c studies Chemical development

FIGURE 3-1. Schematic summary of the development phases and the various processes involved in the discovery, development, and marketing of a new chemical entity. Estimated time requirements and compound attrition rates are also included based on current average numbers (see the text for details). (See insert for color representation of the figure.)

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Target Identification

Assuming that a sufficient understanding of the disease has been achieved, a therapeutic target can be selected: that is, a gene (a DNA sequence) or a protein that plays an important enough role in the development of the disease so that it is reasonable to expect that by modulating its activity the diseased state can be altered. By looking at the targets of existing drugs, proteins that are most likely to serve successfully as drug targets are receptors [especially G-protein-coupled receptors (GPCRs)], enzymes, ligand- or voltage-gated ion channels, cytokines and growth factor, and transporters [5,6]. It is important that the target be druggable, that is, it has to be able to interact in a sufficiently strong and specific manner with a prospective drug. Not surprisingly, the number of druggable targets is quite limited. Target Space: How Many Drug Targets Are There? It is estimated that the currently existing small-molecule drugs target only about 1% of the human proteome, which following the completion of the human genome project turned out to contain a somewhat unexpectedly low number of unique proteins, only about 25,000 [7]. Furthermore, analyses of the now quite extensive collection of protein structures available in the Protein Data Bank suggest that only about 10% of these (approximately 3000) are druggable in the sense that only they possess protein folds that favor interactions with drug-like chemical compounds (i.e., possible ligand-binding sites) [8–10]. In addition, not all druggable proteins represent potential drug targets, as only about 10% of all genes seem to be actual disease-modifying genes; hence, only the intersection of these two subsets are true potential drug targets, with only an estimated 500 to 1500 members (Figure 3-2) [8,9]. Biologics, which are protein-based therapeutics such as antibodies or recombinant proteins, may provide an alternative in some cases, as they can interact along much larger surfaces, not just druggable binding pockets, and they have been pursued increasingly during the last couple of decades. However, they usually cannot cross membranes to reach intracellular targets, leaving only the estimated less than 10% of all human proteins that are resident on the cell surface or are secreted as feasible targets (Figure 3-2) [8,10]. Protein–protein interactions represent a large number of alternative targets; however, they are much more difficult to target with small molecules, as the corresponding interacting surfaces are relatively large, flat, and tend to lack pockets suitable to bind small molecules with sufficient affinity. Nevertheless, the sheer number of such interactions, estimated to be in the range of 650,000 for humans (human interactome) [11], implies that ultimately, a significant number of them should still be druggable, but this is still a relatively novel field [12,13]. Historically, the pharmaceutical industry’s success rate for different target types was, in decreasing order, along the following lines [14]: GPCRs (small ligands), enzymes (small substrates), ion channels, nuclear receptors, proteases, enzymes (large substrates), GPCRs (large ligands), cytotoxic/other, protein kinases, and protein–protein interactions. From a profitability point of view, an important aspect for Big Pharma, which performs most of the research, it also has to be noted that of the approximately 400 known disease entities, only about 50 are considered commercially attractive by today’s requirements of return on investment [14].

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Druggable targets (small molecule) ≈ 500 – 1,500

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Cell surface target ≈ 4,000 (?)

(has adequate ligand binding site suitable (biologics – an
bodies, recombinant proteins – to bind drug-like compounds) can only reach cell surface targets)

Targets of exis
ng drugs ≈ 250

FIGURE 3-2. According to current estimates, druggable disease-modifying targets represent only a relatively small subset of the total human genome, and existing drugs target only a fraction of them [8,9]. (See insert for color representation of the figure.)

3.1.3

Target Validation

As its name implies, the target validation phase follows target identification and serves to affirm that the established target can indeed play a disease-altering role and that it is susceptible to modulations via ligand interactions. During this phase, the therapeutic promise of the target selected has to be confirmed by several cell-based as well as animal models of the disease. The existence of good, validated animal models is of crucial importance. Unfortunately, several important human diseases, such as psychological disorders, neurodegenerative diseases, and autoimmune diseases, still lack good and reliable animal models. For example, whereas there are already about 200 successful therapeutic interventions in nonobese diabetic (NOD) mice [15,16], the most commonly used animal model [17] of type 1 (insulin-dependent or juvenile-onset) diabetes mellitus, therapeutic success has been elusive in humans. An increasing number of large-scale human clinical trials have failed to stop the progressive decline of the function of the insulin-producing pancreatic ␤-cells [18], and even the most successful interventions identified to date, such as cyclosporine (1-15, Figure 1-1) anti-CD3 antibody (teplizumab, otelixizumab), anti-CD20 antibody (rituximab, 1-19), or CTLA-4–B7 costimulatory blockade (abatacept), were only able to achieve an approximately six-month delay in the decline of function [18]. Only after the success of the target validation phase can the actual drug discovery part of a project be initiated with a confirmed, druggable target at hand. 3.1.4

Target-to-Hit and Hit-to-Lead Development

Target-to-hit and hit-to-lead are probably the most commonly recognized actual drug discovery phases. They are focusing on the identification of (new) molecules that can

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interact sufficiently strongly with the target selected to lead ultimately to a structure that can become an approved drug. Drug design processes are typically iterative processes consisting of multiple design → test → analyze results → redesign → retest cycles whereby the information accumulated in each cycle is incorporated continuously into the next cycle. For a brand new target, the first goal is to find a molecule that can affect the intended target at a desirable potency, called a hit. If this can be achieved, the next phase (hit-to-lead) focuses on establishing a promising enough lead compound (and perhaps several backup compounds) that can act on the target in a sufficiently active manner to alter the disease course while also being sufficiently specific, nontoxic, and drug-like. A lead is a prototypical chemical structure or series of structures that demonstrate activity and selectivity in a pharmacological or biochemically relevant screen, and around which the work of further optimization is centered and focused. If even a relatively weak hit can be identified, most large pharmaceutical companies have enough financial resources and medicinal chemistry expertise to improve the binding affinity and selectivity sufficiently, typically with a team of 5 to 15 chemists and within a year or so. If the lead compound survives the various challenges throughout years of testing, it can ultimately become a new medicine. There are several possible approaches to search for such compounds; some of the more important are reviewed briefly here. Natural Sources For most of human history, nature was the main source used in the search for possible therapeutic agents, and even until relatively recently, natural compounds were one of the main sources of potential pharmaceutical agents. Following the success of penicillin, for example, there was a period of systematic search of fungi and soil samples from all over the world, and natural sources undeniably led to several important medicines (cyclosporine, paclitaxel, rapamycin, and vancomycin, to mention only some of the more recent examples). Although nature is still a major source of new drugs [19], the investigation of natural products as sources of new drugs reached its peak somewhere around 1970 to 1980 in the Western pharmaceutical industry [20], when its role started to decrease compared to approaches relying on screening technologies and fully synthetic or biological methods. Of the new chemical entities (NCEs) approved by the U.S. Food and Drug Administration (FDA) between 1981 and 2002, 5% were natural products and 23% were natural product–derived molecules. In a few areas, such as cancer or antihypertensives, their percentage is much higher [21]. Furthermore, another 24% can be considered as “inspired by” natural products [21], and natural products and related drugs can be used in the treatment of 87% of all categorized human diseases, including antibacterial, anticancer, anticoagulant, antiparasitic, and immunosuppressant agents, among others [21,22]. De Novo Design (Structure-Based Design) With some information to start with, medicinal chemists can design new structures that are likely to be active, and by using the iterative design → synthesize → test → analyze results → redesign approach to prepare and select new structures, valuable new compounds can be obtained. During the last two to three decades, this approach has been boosted in particular by the increasing availability of structural data for target molecules. There are now close to 100,000 three-dimensional structures, typically obtained by x-ray crystallography

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or nuclear magnetic resonance (NMR) spectroscopy, available in the Protein Data Bank and freely accessible through the Internet (www.wwpdb.org). These make possible a structure-based drug design approach. Solving the structure of bound ligand–receptor complexes, searching for potential leads that possess the required structural and chemical properties, and then testing for activity (structure-based drug design), has certainly been one of the quiet revolutions of the past years [23–28]. Along these lines, it also helps that the virtual screening of compound libraries by consecutive hierarchical filters, including flexible docking into the protein’s binding pocket, is starting to become sophisticated enough to produce successful leads [29,30]. One still cannot expect to predict absolute binding affinities for unrelated compounds by the use of docking methodologies, but the prediction of relative binding affinities for related molecules is possible, and the use of this approach in combination with databases of available compounds has already made an impact [26]. A review of docking programs and scoring functions found that all of the docking programs were able to generate ligand conformations similar to crystallographically determined protein–ligand complex structures for at least some of the targets, but for the prediction of compound affinity, none of the docking programs or scoring functions made a useful prediction of ligand-binding affinity [31]. Despite much progress, the current state of virtual screening is still not as sophisticated as it might be expected [32], probably due primarily to problems related to addressing receptor flexibility and due to the lack of descriptors able to capture structural properties relevant to binding. Nevertheless, hundreds of free resources are already available online to assist structure-based virtual ligand screening [33]. One of the earliest successful examples claimed for structure-based drug design is the angiotensin-converting enzyme (ACE) inhibitor captopril, which was approved by the FDA in 1981. More recent examples, which were clearly designed on the basis of three-dimensional structures, include, for example, dorzolamide, a carbonic anhydrase inhibitor approved as an antiglaucoma agent in 1995; nelfinavir, a protease inhibitor approved as an antiretroviral agent for the treatment of human immunodeficiency virus (HIV) infections in 1997; and zanamivir, a neuraminidase inhibitor approved for the treatment and prophylaxis of influenza in 1999 [34]. Obviously, the target receptor or its structure is not always known, and receptorbased drug design procedures cannot be applied. Nevertheless, rational, computerassisted pharmacophore-based drug design procedures that are suitable in such cases also made considerable advances, and computer-based de novo design had become reality [27]. Similarity searching, a virtual screening paradigm that attempts to identify chemical structures that are similar in some medicinal chemically meaningful way to a query molecule of interest, is also becoming increasingly successful in identifying novel, promising compounds [35,36]. High-Throughput Screening More recently, high-throughput screening (HTS), which has been made possible by developments in the miniaturization of test assays, robotics, and automatization to allow the testing of millions of compounds [37–39], has become one of the most popular methods in searching for hits for a given target. HTS now plays a central role in drug discovery and it revolutionized

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assay technologies and methodologies; however, it is also undeniable that, in many ways, this technology has not lived up to its original expectations [40]. For example, an analysis of drugs launched in 2000 found that HTS still had no significant impact on the number of drugs launched [41]. Nevertheless, some of the more recently approved drugs that have their origins in HTS hits include, for example, gefitinib (targeting tyrosine kinases in cancer; FDA approval in 2003), sitagliptin (targeting proteases in diabetes; 2006), maraviroc (targeting the gp-120 GPCRs in HIV; 2007), or eltrombopag (targeting cytokine receptors in thrombocytopenia; 2008) [39]. Attempts are being made continuously to overcome the problems of HTS approaches; for example, the development of high-content screening assays that incorporate more sophisticated cellular image analysis [42] or the development of screening assays directly with small animals such as fruit flies or zebrafish [43]. Biotechnology With the development of biotechnology tools, larger and more complex molecules are playing an increasing role, as illustrated by the increasing proportion of approved biologics compared to the traditional small-molecule drugs (Figure 1-3). For example, the development of antibodies [infliximab (Remicade), adalimumab (Humira)] and fusion proteins [etanercept (Enbrel)] inhibiting the binding of tumor necrosis factor (TNF) to its receptors is one of the few recent drug discovery success stories resulting in therapies for chronic inflammatory and autoimmune diseases such as rheumatoid arthritis, psoriasis, and Crohn’s disease [44]. The introduction of rituximab (Rituxan; 1-19), trastuzumab (Herceptin), human growth hormone (Nutropin, Humatrope, etc.), or tissue plasminogen activator (Alteplase) are also important biotechnology successes. Protein therapeutics, which now include antibodies as well as fusion proteins, are of obvious interest, as they are highly specific for their molecular targets, can affect targets that cannot interact with small-molecule drugs (e.g., protein–protein interactions [12,13]), and many of them (e.g., antibodies) can be very stable in human serum, having a relatively long elimination half-life [45]. However, as any protein therapy, they also suffer from several hindrances, such as (1) solubility, route of administration (lack of oral bioavailability), distribution, and stability problems; (2) the possibility of a strong immune response that the body can mount against foreign proteins; (3) increasing regulatory issues due to a number of problems in recent clinical trials; and (4) the possibility of large development costs [46]. Exploiting Existing Information: “Me-Too” Drugs Obviously, a good part of drug discovery and development is not directed toward entirely new targets; instead, it explores some already existing information. As discussed elsewhere, it is very unlikely that some particular biological activity of interest is unique to a single molecule; hence, analog design is a strategy often pursued to obtain a proprietary compound reproducing the activity of a known competitor. Not surprisingly, of the 361 NMEs approved by the FDA between 1989 and 2000, 76% targeted a precedented drugged domain and only 6% targeted a previously undrugged domain [5]. The list of commercially available ␤-blockers is illustrative, with new ones being introduced almost continuously since the discovery of propranolol (1-13) in the late 1960s. The list,

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in chronological order, includes compounds such as alprenolol, oxprenolol, timolol, metoprolol, acebutolol, penbutolol, betaxolol, bisoprolol, esmolol, carvedilol, and nebivolol [47]. Interestingly, even throughout the 1990s, drugs based on novel targets created less value on average than drugs based on precedented targets, and they even had a higher risk of failure [48]. For example, it is a quite common practice to attempt to modify an existing drug or a disclosed lead to establish a different proprietary compound while, hopefully, also improving some property. This approach results in “me-too” drugs, and is frequently criticized, as it lacks true originality; however, often, sound financial arguments support such an approach, chemical inventivity is still needed, and at least every once in a while, the resulting compound may still turn out to show some unexpected new activity (e.g., imipramine, which was designed as an analog of chlorpromazine, showed much more activity against depressive states than against psychoses and opened a new approach to the treatment of depression). Serendipity and Exploiting Side Effects Another frequent source of leads is observation of unexpected side effects. There are countless examples where they resulted in a useful therapy, whether starting from a side effect noted during clinical testing or from a side activity noted earlier during laboratory or animal testing. The evolution of sulfonamide drugs is probably a nice illustration, as after their introduction in the late 1930s as antibiotics (with sulfanilamide as the main active moiety), clinical observations followed by medicinal chemistry optimizations resulted in new hypoglycemic agents, diuretics, and antihypertensive drugs during the 1940s and 1950s [49]. Whenever discussing rational drug design, it is always useful to remember that a partial list of drugs discovered by accident or by what is more accurately designated as serendipity (i.e., when the importance of the accidental observation is recognized and exploited in the sense of Pasteur’s remark that “in the field of observation, chance only favors the prepared mind”) includes, among others [50]: acetanilide, acetylsalicylic acid, amphetamine, artificial sweeteners (saccharin, cyclamate, aspartame), chlordiazepoxide, chlorpromazine, cisplatin, clonidine, cromoglycate, cyclosporine, diethylstilbestrol, diphenhydramine, disulfiram, etomidate, griseofulvin, haloperidol, heparin, imipramine, isoniazid, lithium carbonate, lysergide (LSD), meprobamate, mifepristone, minoxidil, nalorphine, nitrogen mustard, nitroglycerine, nitrous oxide, penicillin, phenylbutazone, prednisone, propafenone, sildenafil, sulfonamides, tamoxifen, urethane, valproic acid, and warfarin. In fact, there is a specific drug design strategy designated as selective optimization of side activities that is focused on transforming some observed “side activity” into the “main activity” by modifying the structure using established medicinal chemistry approaches while also eliminating (or at least reducing) the initial main activity [51]. 3.1.5

Early Distribution and Safety Tests

As reviewed briefly in Section 2.2, any sufficiently promising compound has to be able to reach its designated target in sufficient concentrations to produce its desired effect. For this, it has to overcome many obstacles; it has to bypass different cells, membranes, and metabolizing enzymes, not to mention problems related to solubility,

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FIGURE 3-3. The major reasons for attrition of investigational new drugs during clinical trials and their change during 1990–2000 (data after [57]). Whereas inadequate pharmacokinetic and bioavailability results were the major reason (ca. 40%) in 1991, they were reduced drastically by 2000, so that lack of efficacy (ca. 30%) and safety (toxicology and clinical safety for an approximate total of another 30%) became the major factors causing attrition. (See insert for color representation of the figure.)

stability, partition, distribution, and other challenges. The compound also has to have an acceptable duration of action and cannot be toxic (even in the sense that it cannot have undesired side effects). Therefore, testing for absorption, distribution, metabolism, excretion, and toxicological properties (ADME/Tox) is an important part of lead selection. It usually involves computational (in silico) screening followed by testing in cell-based and other assays (in vitro) as well as in live animals (in vivo). Before focus turned to these issues around the 1990s [52], inappropriate bioavailability and pharmacokinetics (PK) was the single major source of failure of putative drug candidates during their clinical trials (ca. 40%) [53,54]. Following this realization, it is now standard practice to address physicochemical, pharmacokinetic, and biopharmaceutical properties early during the drug discovery and development process [55,56], an approach that has been tentatively designated as property-based drug design [56]. Accordingly, by 2000, failure due to inadequate PK has been reduced from 40% to 10%, leaving lack of efficacy (ca. 30%) and safety (toxicology and clinical safety for a total of about another 30%) as the main reasons for failure (Figure 3-3) [57]. To convert a candidate compound into a successful drug, good ADMET/Tox properties are probably just as important as adequate in vitro pharmacodynamics potency [58]. Since the focus of the present book is on metabolism-based drug design, it is important to note that of the four ADME processes that determine PK characteristics, metabolism is possibly the most complicated. Undoubtedly, an important reason for this is the variety of enzymes that are involved. As discussed earlier, metabolism is also an important process; it is the listed clearance mechanism for about 75% of the top 200 drugs (Figure 2-4). For most investigational drugs, metabolism is difficult to predict, and detailed preclinical studies are needed. A recent study using human

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oral bioavailability for more than 750 chemical compounds has concluded that whereas rules based on molecular properties can be used as effective filters for the estimation of oral absorption, especially intestinal absorption of passive diffusion, they cannot be used for oral bioavailability, and no simple rule based on molecular properties can be used as general filters to predict oral bioavailability with acceptable confidence [59]. 3.1.6

Lead Optimization

The lead optimization phase is focused on the exploration of the chemical space around an existing lead compound in an attempt to optimize its properties. Depending on the time and financial resources available, hundreds of structural variations and analogs may be prepared and tested to improve the balance of properties needed for clinical success. There are many well-established medicinal chemistry strategies to perform molecular variations (e.g., addition or removal of methylene units; addition or removal of unsaturation; addition or removal of a ring or other steric bulk; addition, removal, or repositioning of substituents; altering the size, electronegativity, or hydrogen-bonding ability of substituents; ring transformations; isosteric replacements; conformational restrictions; and others) and explore structure–activity relationships; detailed descriptions can be found in the literature (e.g., [60–64]). Specific goals may vary from project to project, but in general, the following are expected from a small-molecule candidate in order to be considered as promising enough for development [65]:

r Potency. The compound has to have a sufficiently high affinity for its target; in general, a median effective concentration in the less than 100-nM range is desirable (EC50 < 100 nM, corresponding to pK B > 7). It is worth noting again that most currently approved small-molecule drugs are quite active; a recent study of all binding affinity–related endpoints of all identifiable drug-efficacy target pairs (i.e., IC50 , EC50 , ED50 , Ki , Kd , and pA2 ) found that most of them have affinities (potencies) in the nanomolar range and that the overall median affinity is around 20 nM (Figure 2-2) [5]. r Selectivity. If feasible, at least 100-fold selectivity over other pharmacological targets of interest is desirable. Achieving adequate selectivity is an important and often difficult goal in early drug discovery. As an interesting side note, however, it is worth noting that it is being recognized increasingly that many existing drugs, in fact, are not highly specific for a single target but show clinically relevant polypharmacology, and their clinical effects are mediated through modulation of a set of protein targets [5]. r Specificity. A specific well-defined mechanism of action is to be expected; for example, competitive receptor antagonism is a frequent mechanism of action, but noncompetitive (allosteric) mechanisms might also have advantages [66]. r Adequate solubility and partition properties. These properties are needed for the compound to be able to reach its intended target at sufficiently high levels

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(especially if oral administration is pursued). Fortunately, these can be predicted relatively reliably from molecular structure alone; hence, many virtual compounds can be filtered out even before synthesis (see our discussion of the rule of five in Section 2.3.2 and related discussion). Acceptable oral bioavailability and duration of action. Targets are somewhat malleable, but an oral bioavailability of at least 30% (F% > 30%) and an elimination half-life of at least several hours (t1/2 > 4 h) is a realistic goal. In vivo potency. Potency has to be adequate in living organisms as well; for example, one can expect a dose of 1 to 10 mg/kg to produce measurable effects. This requires a successful combination of good pharmacodynamic activity (potency) and adequate ADME behavior. Nontoxicity. There should be no effects in acute toxicity and in genotoxicity studies; in the long run, the compound will also have to survive chronic toxicity studies (i.e., stay “clean” in toxicology). It is also increasingly important to have compounds that are unlikely to be the subject or to create drug–drug interactions (e.g., if possible, they should not be metabolized by CYP3A4) [67]. Patentability. Intellectual property (IP) rights are of great importance (to recover the costs of development, including those of the many failed projects that did not result in a marketable drug). Adequate protection usually requires a comprehensive IP portfolio based on a composition of matter patent (e.g., a novel chemical structure or a new combination), as patents protecting just the process of production or a new use can be circumvented relatively easily [68]. Manufacturability. The final product has to be preparable in a reproducible manner and at a reasonable cost of goods; hence, a scalable synthetic route has to exist. PRECLINICAL DEVELOPMENT Preclinical Testing

To be able to initiate tests in humans, a large number of preclinical (i.e., animal) tests have to be completed. By the end of this phase, the field of candidate drugs in the project should be winnowed down to very few—preferably only one with a couple of backup compounds in reserve. Part of the preclinical tests are performed to make sure that the candidate is indeed promising enough to be moved forward into the much more expensive clinical trials, and part of them are performed to satisfy the ethical and strict regulatory requirements of human testing. To avoid any possible problems, regulatory agencies such as the Food and Drug Administration (FDA) in the United States or its corresponding counterparts in other regions [e.g., the European Medicines Agency (EMA) in the European Union] require extremely thorough testing before a candidate drug can be studied in humans. In fact, drug development is no doubt the most regulated of all human activities [14]. Traditionally, first-in-human clinical trials require repeated dose toxicity studies in rodents and in nonrodents that are expected to identify dose levels leading to toxicity

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as well as dose levels leading to no toxicity (called the no-observed adverse effect level, NOAEL) in both species (most typically, rats and dogs). Evaluations of the mutagenic potential and of the effects on reproductive performance (in two species; usually, one rodent and rabbits) are also required. Performing these can consume up to one or two years, especially as considerable, probably up to kilogram amounts of test substance could be required; hence, a first scale-up of the synthesis usually also has to be worked out. As a relatively recent development, in certain cases part of this can be avoided, and exploratory clinical trials can be conducted even without detailed animal results. For example, so-called phase 0 trials can be performed with microdosing to assess PK and distribution in humans by using very low doses (less than 100 ␮g and less than 1/100 of the expected pharmacologically active dose). This has been made possible by technological advances such as accelerator mass spectrometry, which is able to detect 14 C-labeled drugs and metabolites in biological samples with up to subattomole (10−18 mol) sensitivity [69]. 3.2.2

Investigational New Drug Application and Safety

Before any testing in humans can be initiated (i.e., before any clinical tests can be set up), an Investigational New Drug (IND) application must be filed with and reviewed by the FDA. According to current regulations, the IND has to include information in three main areas: (1) chemistry and manufacturing control (CMC; i.e., chemical structure and composition, production methods, stability, and controls used for manufacturing), (2) animal pharmacology and toxicology (detailed preclinical data supporting the safety of the investigational compound as well as its mechanism of action and activity), and (3) clinical protocols and investigator information for the proposed clinical trials. These are needed to ensure in as comprehensive a manner as possible that the subjects who will participate in the proposed trials will not be exposed to any unreasonable risks. In addition to the IND application, all clinical trials must be reviewed and approved by an appropriate institutional review board at the institutions where the trials will take place, and it is also a requirement to have appropriate informed consent forms that have to be signed by all subjects participating in the clinical trial. It is worth remembering that introduction of these procedures requiring strict rules for the investigation of new drugs and informed consent of study subjects date back to the Kefauver–Harris amendments [70], which were introduced in the United States in 1962 after one of the biggest tragedies of modern medicine, the thalidomide tragedy, which caused serious birth defects such as phocomelia in approximately 10,000 newborns, mainly in Western Europe. Thalidomide (Contergan) was introduced and marketed by Gr¨unenthal (Germany) as a sedative, but was also found to be an effective antiemetic and was used, unfortunately, by many pregnant women to relieve their symptoms of morning sickness before its toxic effects were identified. It is probably a good indication of how desperately effective therapeutics are needed that about 40 years later, thalidomide has been approved by the FDA (with strict monitoring guidelines)—this time, for the treatment of erythema nodosum leprosum, a complication of leprosy (1998), and for the treatment of multiple myeloma in

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combination with dexamethasone (2006). Unfortunately, despite all efforts, errors are still possible, and fraud cannot be fully avoided in academic or industrial research. During the last decades, there seems to be an increasing number of scientific and clinical publications judged so flawed that they were retracted, and many patients (several thousands) were put at risk by studies based on them [71–73]. 3.3 3.3.1

CLINICAL DEVELOPMENT Phase I Clinical Trials

Phase I trials are the first clinical testings in humans and are designed with the main goal of assessing the safety (pharmacovigilance) and tolerability of the new compounds. Phase I studies typically involve 20 to 100 healthy volunteers and are usually conducted in an inpatient clinic to allow full-time monitoring of the subjects. Normally, they involve dose ranging, such as single or multiple ascending-dose studies to establish an appropriate, safe therapeutic dose. The pharmacokinetics is usually also assessed (absorption, distribution, metabolism), and, if possible, some pharmacodynamics monitoring is also performed to collect any possible indication of potential effectiveness. Notably, about 30% of all trials conducted by Big Pharma are proof-ofprinciple trials intended to show that the candidate compounds hits the right target, produces the desired effect, and is safe enough to be worth pursuing further [14]. 3.3.2

Phase II Clinical Trials

Phase II trials are intended to evaluate the effectiveness and safety of the candidate drug in the targeted patient population, and they typically involve several hundred patients. They are also used to optimize the dose level and the administration schedule. For scientific rigor, currently it is standard practice to consider reliable only results obtained in clinical trials that are placebo controlled, randomized, and double blinded. Placebo controlled means that some subjects will receive the new drug candidate while others will receive a placebo (from the Latin “I shall please”)—an identicallooking but inert treatment—to control for the placebo effect, the expectancy effect that could occur just due to receipt of medical treatment. It is now well recognized that a significant placebo effect is present in many diseases [74,75]. Randomization is needed to avoid any possible (conscious or unconscious) bias in subject assignment; therefore, study subjects enrolled in the trial are assigned in a completely random manner to one of the treatment arms, and typically, special care is imposed to avoid any possible influence by those involved; for example, the random assignment is usually done off-site, not at the clinic where the patients and the physicians are. Double blinding is needed to avoid any possible bias in treatment administration; therefore, neither the researchers (including the physicians administering the treatment) nor the subjects know who is receiving what treatment until the study is over. Studies are also expected to have either a parallel or a crossover design. All these are intended to minimize the possibility of any even unintended influence and to ensure that the most realistic measure of the effect of the test compound is obtained. The design of

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clinical trials has many other important aspects that require careful attention [76]; for example, it is also of particular importance to have a correct number of subjects enrolled (i.e., to correctly establish the “power” of the trial). There have to be enough subjects to establish statistical significance, but one also wants to avoid overpowering the trial to reduce the unnecessary time and financial expenses due to an unneeded large number of subjects enrolled. Even though there is no official definition, it is becoming increasingly common to use further subdivisions (e.g., phase IIa trials, which involve more limited patient populations and are usually designed to assess dosing requirements, vs. phase IIb trials, which involve larger patient populations and resemble smaller phase III trials designed to assess efficacy at the dose selected). This trend is particularly popular among biotechnology companies, and although it has some justification, it is also at least driven partially by a phase inflation intended to impress investors [77]. 3.3.3

Phase III Clinical Trials

Finally, regulatory approval also requires testing in phase III trials that involve a large number of patients (overall, a total of 1000 to 5000) at multiple sites. Such large-scale studies are needed to generate statistically significant data about the safety, efficacy, and the overall benefit–risk relationship of the drug candidate. Results obtained in these trials also provide the basis for the labeling instructions of the marketed product that are required to ensure the proper use of the drug; they include important information, such as the recommended correct dosage, possible side effects, as well as potential drug–drug interactions. Obviously, phase III trials constitute by far the most expensive part of the drug development, as they have to involve many different clinical sites and hundreds of physicians to reach a sufficiently large and diverse group of patients. In fact, pharmaceutical companies spend about 10 to 15% of their R&D budget on preclinical research and the remaining 85 to 90% on clinical trials and marketing [14]. According to this estimate, for every NCE discovered, developed, and launched, preclinical research consumes about 50 biologist-years and 50 chemist-years, which alone costs $30 million to 40 million at $250,000 per fulltime employee. Compared to preclinical costs, clinical costs are considerably larger; they represent the majority of the final total cost even if the cost of all preclinical failures is included [3]. Because of the considerable financial resources required and because of the complexity of the coordination and management required, there are only relatively few large, established drug manufacturers (usually, those known as members of Big Pharma) that can undertake such tasks. Because of the large costs involved in phase III trials, most well-managed pharmaceutical companies are well aware that phase II is the critical place to weed out those candidates that are not likely to have a good future based on existing indications of safety and/or efficacy. According to a recent estimate, during the 2002–2008 period, the probability of success to market (i.e., the chance of successful market launch for a drug entering the corresponding clinical phase) were 5 to 10% for phase I, 11 to 17% for phase II, 51 to 66% for phase III, and 84 to 95% for submission, respectively [78]. Obviously, during the time when clinical trials are underway, a number of other important tasks

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(e.g., pharmaceutical development, chemistry scale-up for full production) as well as testings (e.g., chronic toxicity, carcinogenicity) are also performed in parallel (as shown clearly in Figure 3-1) to fulfill all the requirements for the application for regulatory approval. 3.4 REGULATORY APPROVAL AND POSTMARKETING DEVELOPMENT 3.4.1

New Drug Application and Regulatory Approval

To request approval for the marketing of a new drug, the sponsor (i.e., the developing company) has to file a New Drug Application (NDA) with the FDA. Obviously, this is done only if the data obtained from all completed trials look promising enough. For biologicals, filing of a corresponding Biological License Application is required. In addition to all the clinical data, when the drug is intended to be used in humans for prolonged periods, it is also required to have data from chronic toxicity studies (rodent and nonrodent species for six months or more) as well as from carcinogenicity studies (two-year studies in two different species). Because these are time-consuming and quite expensive studies, they are usually performed in parallel with the clinical studies (Figure 3-1). Since the NDA has to include all the information accumulated from previous years of work on the drug candidate, as well as detailed descriptions of its proposed manufacturing and labeling, filing of this application alone is a large task: In the current regulatory environment, NDAs can often run up to 100,000 pages or more. All this information is then reviewed by regulatory experts (the FDA in the United States) to decide whether or not the proposed drug candidate is safe and effective enough to be approved. The review can result in (1) approval as is, (2) straight-out denial, or (3) in an “approvable” letter, which requests specific additional studies and information before a final approval. NDA reviews typically also include evaluation by an advisory committee, which consists of FDA-appointed experts. This reviews the data provided by the applying company as well as the FDA reviewers and then provides a recommendation on whether the application is approvable and/or what additional conditions will have to be fulfilled. As often mentioned in media news reports, the FDA is not required to follow the recommendations of this committee, but it usually does. Just as for the IND, it is worth remembering that introduction of the NDA requirement dates back to another medical tragedy: that of the “Elixir sulfanilamide” in 1937, which caused about 100 deaths in children in the United States due to the use of diethylene glycol as a solvent, which is now known to be highly toxic if ingested (a warning displayed on the label of all engine coolants that contain diethylene glycol). This unfortunate tragedy ultimately resulted in the introduction of the 1938 Food, Drug, and Cosmetic Act, which considerably increased the authority of the FDA to regulate drugs. Since at that time, existing food and drug laws did not require safety studies to be done on new drugs, Massengil Co., the maker of this preparation, could only be fined for $16,000 for mislabeling its product as an “elixir,” as elixirs were supposed to contain alcohol as solvent.

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Manufacturing

A complex but often overlooked aspect of the drug approval process is related to the scale-up of the production from small-scale (laboratory) batches to large-scale (industrial) manufacturing, which often is a considerable chemical challenge, and the many regulatory aspects that have to be satisfied to meet the strict FDA-imposed guidelines for good manufacturing practices (GMPs). Manufacturing drugs that meet all needed purity and quality standards in a fully controllable and reproducible manner is a difficult task, especially because the active pharmaceutical ingredients involved are often highly potent substances, and therefore a high degree of homogeneity and uniformity has to be achieved and maintained consistently. Not surprisingly, to meet all the regulatory requirements, the production of a newly approved drug often involves the construction of a new manufacturing facility or the complete reconstruction of an existing facility. 3.4.3

Postapproval Studies and Phase IV Trials

In general, studies on new therapeutic agents do not stop with their regulatory approval. The marketing companies are usually required to carefully monitor their use and to submit periodic reports, including cases of observed adverse events, to the FDA so that even minor safety or efficacy concerns do not go unnoticed as the number of new users increases. Sometimes, phase IV studies are also imposed by the regulatory agencies to evaluate either the long-term safety of the newly approved medication or its effects in certain specific patient subpopulations. 3.4.4

Patent Expiration and Generic Approval

As long as the original patent is valid, the owner has exclusive rights for marketing the product. With the current legislation, in the United States, patents expire 20 years after the filing date, and regulations are similar in most other industrialized nations. Hence, there is only a limited exclusive marketing time after approval of the NDA. This is the time during which a successful new drug can recuperate the corresponding R&D investments and eventually become a source of profit. Clinical trials alone can consume up to about half of this 20-year period reserved for exclusive rights to development and promotion, which is the main source of profit earning [79]. This is the main reason that those involved in drug development strive continually to optimize the process [3], as saving even a few weeks can result in substantial financial rewards. A relatively recent modeling analysis indicated that for medicines marketed successfully, each six-month delay to launch can mean a loss of almost $100 million in the net present value or a reduction of 0.5 percentage point in the internal rate of return, numbers that are obviously much higher for top-performing drugs [80]. As of 2005, the average effective patent life for major pharmaceuticals was only 11 years [81]. Changes in legislature and medical insurance policy made it much easier for generic competitors, which operate on much thinner profit margins, to cut into the market at the expiration of patent protection, and since then, sales of generic drugs increased

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much faster than sales of branded drugs [82]. In some cases, the FDA can add back the time consumed by its review process to the time of marketing exclusivity; however, the extension cannot increase the total life of the patent to more than 14 years after NDA approval. The pharmaceutical industry relies heavily on patent legislation and the requirements of intellectual property protection significantly influence the R&D process [68]. Practically all the important new drugs developed after World War II were developed by private pharmaceutical companies in industrialized nations that provide strong patent protection (mainly the United States, Germany, Switzerland, France, the UK, and Japan). From a legal perspective, a patent is essentially a piece of property issued by a state that grants the exclusive rights of the claimed invention to the owner in exchange for the disclosure of the details of the invention. From this perspective, inventions can be divided into four main categories [68]: (1) inventions concerning products or machines, (2) inventions concerning processes, (3) inventions concerning the use of products already known for other uses (e.g., a new therapeutic use), and (4) inventions concerning compositions or combinations. For pharmaceuticals, the only truly important patents are the composition-of-matter patents, as those covering only the method of production or only a new use can be circumvented relatively easily. New-use patents are strong patents only if the new use also involves a special and/or new formulation. The pharmaceutical industry has become a significant industry with worldwide pharmaceutical sales approaching $1 trillion. The United States alone represents around half of the worldwide market (Figure 3-4) [83]; hence, the (perceived)

Pharma sales, World (B$, 2006)

4%

9%

9%

North America 48% Europe

Japan 30%

La
n America Australia, Asia, & Africa

Pharma sales, Europe (B$, 2006)

Total: 608 US$ billions

22%

24%

Germany France 9%

20%

Italy

UK 12% 13%

Spain Other (Europe)

FIGURE 3-4. Distribution of worldwide pharmaceutical sales for 2006 (total $608 billion; data from [83]). (See insert for color representation of the figure.)

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demands of the U.S. pharmaceutical market and the regulations imposed by the FDA are major determinants driving the discovery, development, and marketing of pharmaceuticals worldwide (and also a main reason why we focused on reviewing the FDA approval process as an example of regulatory approval). Being able to reach the U.S. market is a major development criterion for any prospective new therapeutic agent. On the other hand, as an interesting side note, there are over 800 compounds sold in Europe that are effective and therapeutically beneficial, that are not registered in the United States and probably never will be because the remaining patent life is not long enough and the FDA approval process is slow enough not to make it worthwhile [14]. As mentioned, many approved drugs, in fact, do not result in a satisfactory return on investment [84], but in a very few cases, there are true blockbuster drugs—those whose annual sales are in excess of $1 billion. After the expiration of the patent protection of a new drug, other companies, which have applied and gotten approval of an abbreviated NDA (ANDA), can produce equivalent generic drug products that are similar enough to the original drug to be used as a replacement in clinical practice. This is established using bioequivalence studies [85], a procedure that was introduced by the Drug Price Competition and Patent Term Restoration Act of 1984, also known as the Hatch–Waxman Act. According to the FDA, bioequivalence is defined as “the absence of a significant difference in the rate and extent to which the active ingredient or active moiety in pharmaceutical equivalents or pharmaceutical alternatives becomes available at the site of drug action when administered at the same molar dose under similar conditions in an appropriately designed study” [86]. The main criteria needed to establish bioequivalence is that the test-to-reference relative ratios (e.g., generic formulation vs. original formulation) for Cmax , AUC0–t , and AUC0–∞ with their 90% confidence intervals have to be within 80 to 125% in the fasting state. Nevertheless, generics may not always be fully equivalent in every sense, as the exact formulations used and other minor but not necessarily unimportant things can be different, as they do not have to be the same. 3.5 3.5.1

PROBLEMS WITH THE CURRENT PARADIGM Decreasing R&D Efficiency

As is obvious from this description, the discovery and development of new drugs (NCEs or NMEs) is a tedious process. For every new therapeutic agent that reaches the market successfully, there are many that fail along the way. As mentioned, despite the tremendous recent progress in our understanding of the molecular mechanisms of many diseases and in the technology applied in pharmaceutical research, the number of new molecular entities launched has essentially been stagnating around 15 to 25 per year since the mid-1960s despite exponentially increasing R&D expenditures (Figure 1-3) [83,87–90]. There are a large number of possible reasons, including the increasing regulatory burden; the unrealistic public expectation of no side effects and abuse potential; the depletion of effective new targets (with possibly no more “low-hanging fruit” remaining); the expectation for any new drug to outperform all existing old ones (many of which are highly effective); the unprecedented

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need for highly multidisciplinary approaches requiring collaborations across very different scientific, technical, regulatory, and business fields; the inability of the increasingly few and increasingly large organizations left in the pharmaceutical and biotechnology field to carry out effective and/or innovative research; the pursuit of speculative targets; the often ill-advised push to advance marginal compounds into late-stage clinical development due to unwillingness to admit failure; and many others (e.g., [90,91] and references therein). Large Attrition Rate Despite strongly rising drug-related spending, caused primarily by the aging of the population, the increasing prevalence of obesity, and the rising quality of life in industrialized nations, the pharmaceutical industry is faced with increasing difficulties and unprecedented challenges to its business model [3,82,92,93]. During drug development, the attrition rate is terrible; one such estimate, according to which out of 5000 to 10,000 new compounds actually synthesized only one reaches the market, is summarized in Figure 3-1. According to a slightly different estimate, from 30,000 compounds synthesized, 2000 enter preclinical development, 200 enter phase I clinical trial, 40 enter phase II clinical trials, 12 enter phase III clinical trials, 8 are approved, and only 1 makes a satisfactory return on investment [84]. Furthermore, even among NCEs launched, there is considerable redundancy and very little real progress. For example, of the 269 NCEs that received FDA approval between 1976 and 1990, only an estimated 15% represented considerable improvement, compared to 49% that represented no or negligible improvement and 35% that represented modest improvement [94]. Accordingly, the pharmaceutical industry, as well as drug and medicinal discovery as such, is in clear need of innovation. Innovative Research vs. Drug Development: Size Matters Bringing a new drug to the market currently requires an average of more than 15 years and costs more than $1 billion (including the price of failed projects [3]). Consequently, as mentioned, there are very few large, established drug manufacturers—collectively known as Big Pharma—that can assume the corresponding risks, can manage the very diverse multidisciplinary teams needed for R&D, and have the required financial resources. An important problem of the drug discovery and development paradigm is that to identify a promising lead and to bring it to market successfully, one has to be able to maneuver through a wide range of scientific areas and manage the interaction of the corresponding experts involving, among others, physiology, pathology, molecular biology, genetics, pharmacology, medicinal chemistry, organic chemistry, formulation science, veterinary medicine, toxicology, pharmacokinetics, statistics, intellectual property law, marketing, clinical trial management, and others. Very few companies can manage all these successfully, and very few people can understand all the necessary aspects to provide effective leadership. In the meantime, however, many big companies are not particularly good at managing R&D innovation, and this might be part of the problem, which is unlikely to be solved by an ever-popular trend for mergers. As a result of mergers and acquisitions performed to maintain income despite decreasing R&D productivity, only 11 of the 42 members of the Pharmaceutical Research and Manufacturers of America (PhRMA) from 1988 survived

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by 2011. The top 10 pharmaceutical companies remained the same during the last decade (2000–2010); in order of market capitalization they were Johnson & Johnson, Pfizer, Novartis, GlaxoSmithKline, Roche, Merck, Sanofi, Abbott, AstraZeneca, Bayer, Bristol-Myers Squibb, and, Lilly [78]. In the meantime, intriguingly enough, one recent study found that the number of NMEs is closely related to the number of companies involved in their development, so that changes in the number of companies explains a very large part of the changes in expected NME output [95]. Not surprisingly, during the past 20 years, small companies, helped by venture capital funding, are enjoying an increasing trend in NME approvals [95]. Increasing Costs In line with the above, a relatively recent analysis combining expenditure estimates with estimates on success rates and durations found (again) the cost of drug development (i.e., the net revenue needed to make investment in new drugs profitable) to be over $1 billion [96]. The average cost of NCE development increased very rapidly in recent years (see Figure 1-3) [96,97]. In fact, an intriguing observation related to this is that the number of new FDA-approved drugs per each (inflation-adjusted) $1 billion of R&D spending in the drug industry has quite consistently halved approximately every nine years since 1950, a sort of reverse Moore’s law [90]. The widely known Moore’s law was originally formulated in the 1960s by Intel cofounder Gordon Moore [98], and it is usually quoted as predicting that the number of transistors that can be integrated into a microchip, and as a result, processor speed, doubles about every 18 months. It is clear that our understanding of biological systems and their complexities is far from our understanding of physics and electronics—something that often tends to frustrate those working in pure technological fields [99,100]. Part of this frustration is supported by a “survivor bias” among R&D projects that creates the illusion of our ability to pursue a much more rational drug discovery and development process than the reality. News stories on the molecular mechanism of action-based rational design of the few successful drugs overshadow the many more stories of unexpected failures of projects that were also initiated on the basis of just as plausible rationales and that had similarly plausible biological stories until the point of their ultimate failure [90]. New technologies and increasing regulatory demands have pushed R&D expenditures in the pharmaceutical industry close to 20% of sales from its value of 4% in 1966. For comparison, the comparable current value of R&D costs is only 5 to 6% in the electronics industry, which is also heavily R&D dependent [101]. On the other hand, it has to be mentioned that in the context of spiraling health care costs, the sometimes large profits of pharmaceutical companies also subjected Big Pharma to much anger from the public despite the fact that pharmaceutical costs represent only about 10% of total health care costs—a sentiment only acerbated by an increasing number of uncovered stories of ruthless product promotions [102,103]. In the United States, health care costs increased at an incredible rate: adjusting for inflation and purchasing power parity, total per capita expenditures increased from $356 in 1970 to $2810 in 1990 and $7538 in 2008 [104]. Related to this, it also has to be mentioned that expenditures on prescription drugs are one of the fastest-growing components of national health care spending, rising in the United States almost threefold between

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1995 and 2007, so that their share of total health care spending increased from 6% in 1995 to over 10% in 2007 [105]. This growth in prescription drug expenditures has coincided with the growth in pharmaceutical promotion, which in the United States increased from $11.4 billion in 1996 to $29.9 billion in 2005. At least a considerable part of this is due to rules that now allow broadcast direct-to-consumer advertising [105], which currently is allowed only in the United States and New Zealand. It is also undeniable that the large profit margin of pharmaceutical and biotechnology companies, which earn close to 20 cents per sales dollar (compared to, e.g., about 18 for banks, 14 for semiconductor makers, and 9 to 10 for the oil industry) is difficult to explain in the long run [106]. More scrutinous analyses of the Big Pharma companies found that 27 to 31% of total revenues are used for sales, marketing, and administration; 18 to 20% as profit; and only 11 to 13% for R&D [102]. This clearly reveals an increasing tendency to spend money and effort primarily on the development, promotion, and defense of molecules of minimal worth (including many “me-too” drugs) and avoid riskier but possibly groundbreaking research [102,103]. New Technologies: Which One Is Worth Pursuing? During the last half-century, drug research and development has seen tremendous technological progress, with many changes that are measurable only in orders of magnitude. For example, DNA sequencing is about 109 faster than it was in 1970s when the first genome sequence was determined; protein structure determination (e.g., x-ray crystallography) is about 103 more efficient than it was in the 1960s; using combinatorial chemistry, a synthetic chemist can prepare around 103 times more compounds in the same time than could be prepared in the 1980s; and high-througput screening (HTS) reduced the cost of testing compound libraries by at least 101 since the mid-1990s [90]. Unfortunately, many of these new technical developments ultimately failed to materialize in an increased NCE output. For example, an analysis of drugs launched in 2000 revealed that not only did HTS still had no significant impact, but most of the drugs launched were in fact derived by modifications of known drug structures or published lead structures, and the final drugs were very closely related to their original leads [41]. In fact, it was even suggested that that the new techniques may be generating bigger haystacks as opposed to more needles [107]. For example, a notable problem with HTS approaches is that the leads identified still need structural optimizations to improve their activity and specificity profile; however, most commercially available compound libraries used in these screenings often do not lend compounds susceptible to easy synthetic manipulation—a problem that is only compounded by what has been termed the rampant “rotovapophobia” of the biological communities usually driving these efforts [108]. Attempts are being made to overcome some of problems of the traditional HTS assays, and they include, for example, the development of highcontent screening assays that incorporate more sophisticated cellular image analysis or the development of screening assays with small animals, such as the nematode Caenorhabditis elegans, the fruit fly Drosophila melanogaster, and the zebrafish Danio rerio [43]. It is becoming clear that despite great technical advances, functional, particularly in vivo, pharmacology cannot be skipped and has to be fully integrated into any drug

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discovery process, as there seem to be no easy shortcuts to creating significant new drugs [109]. It is also increasingly being recognized that many effective drugs are, in fact, not highly specific but, rather, show clinically relevant polypharmacology (i.e., they are “dirty” drugs in the sense that they bind to multiple target proteins and their clinical effects might be mediated through modulation of a set of protein targets) [5]. Strangely enough, many approved drugs seem to show considerable polypharmacology even on unrelated targets, as was confirmed in a recent screening—perhaps providing further support for the concept of drug-like structure [110]. Another important aspect worth noting is related to the observation that, as should be clear by now even from the present brief review, any ultimately successful drug candidate has to survive various hurdles, imposed by preclinical tests, animal toxicity, clinical safety, clinical efficacy, organizational strategy, patentability, regulatory approval, marketability, and many others. Consequently, as well illustrated by a Tour de France analogy [111], a candidate compound that is not particularly outstanding in any individual aspect but is a consistently good performer in all of them may very well become the ultimate “champion”. Therapeutic Areas: Which One Is Worth Pursuing? The vagaries of new drug discovery are well illustrated by the apparent lack of correlation between funding per therapeutic area and the number of new drugs discovered. Whereas biomedical research funding (both public and private funding in the United States) is correlated to a good degree with disease burden, as measured by disability-adjusted life years (especially for the disease burden in high-income countries), it shows no close association with an increase in new therapies, even when incorporating a lag of eight years between funding and new drug approvals (Figure 3-5) [112]. Of course, real innovations or discoveries are those that cannot be predicted, and the drug market is particularly unpredictable. Most blockbuster drugs, which are usually defined as those having annual sales in excess of $1 billion, were not only not forecast by the respective marketing departments, but were even not supported strongly at the time of target selection by the respective research managers [113]. Examples include now well-known compounds such as cimetidine (Tagamet), tamoxifen (Nolvadex), fluoxetine (Prozac), and atorvastatin (Lipitor). It is also undeniable that clearly frustrated by their continuing failures to find drugs useful for treating serious diseases such as cancer or neurodegeneration and by continuous pressure to generate profits, pharmaceutical companies have increasingly turned toward “lifestyle” drugs [114]: fluoxetine (Prozac; 1-17) for depression, minoxidil (Rogaine) for hair loss, sildenafil (Viagra; 1-18) for erectile dysfunction, bimatoprost (Latisse) for eyelash lengthening, and botulinum toxin (Botox) for cosmetic procedures. Bimatoprost, a prostaglandin analog prodrug originally introduced to lower intraocular pressure in the management of glaucoma, is now also approved for cosmetic use (Latisse; FDA approval in late 2008) after it was noticed to cause the growth of longer eyelashes in glaucoma patients. Minoxidil, originally introduced as an oral medication to treat high blood pressure, is now used much more widely as a topical solution for the treatment of male pattern baldness (androgenic alopecia) after it was noticed to promote hair growth as a side effect. Sildenafil was originally

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250

Disease Burden, Dev. World (Disability-Adjusted Life Years, millions)

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Neuroscience R² = 0.5

Infec
ous diseases

40 35

200

Cardiovascular

30

150

R² = 0.8 HIV/AIDS

25 Oncology

20

100

Respiratory

15

Gastrointes
nal

10

50

Endocrine Genitourinary

5 0 0.0

2.0

A

4.0 6.0 8.0 10.0 12.0 Total Research Funding (US$ Billions)

14.0

0 16.0

Total Disease Burden (Disability-Adjusted Life Years, millions)

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Total New Drugs Approved by FDA (1996-2005)

200 Neuroscience

180 Endocrine Infec
ous diseases

160

R² = 0.3

140 120 100 Gastrointes
nal Respiratory

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Cardiovascular

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Oncology HIV/AIDS

40 Genitourinary

20 0 0.0

B

2.0

4.0 6.0 8.0 10.0 12.0 Total Research Funding (US$ Billions)

14.0

16.0

FIGURE 3-5. Whereas biomedical research funding is aligned to a good extent with disease burden by therapeutic area, especially with that in the developed world (A; diamonds; r 2 = 0.8), it is not closely associated with the number of new drug approvals (B; triangles, r 2 = 0.3, but even this is due primarily to the neuroscience data point). The research financing is measured by total financing (i.e., public plus private, United States, 2005), and the disease burden is measured by the disability-adjusted life years, both for high-income countries (diamonds, left axis) as well as for the total world (circles, right axis) (2015, estimated; data after [112]).

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intended for the treatment of hypertension and angina pectoris (a symptom of ischemic heart disease), for which it showed no particular effectiveness in the first clinical trials. However, it proved quite popular among the (male) subjects recruited for these trials, due to its particular side effect, which was cleverly recognized by its developers at Pfizer, and cGMP-specific phosphodiesterase type 5 inhibitors (sildenafil, vardenafil, tadalafil) are now a well-known class of drugs (e.g., Viagra, Levitra, Cialis, respectively) used in the treatment of erectile dysfunction. 3.5.2

The Drug Discovery Process: Improvements Needed

Focus on the Therapeutic Index A main reason behind the low success rate of most drug design processes is that while focusing on increasing pharmacological potency, they often tend to ignore the related side-effect and toxicity aspects. Many new therapeutic agents designed to bind to a specific receptor or found to have high activity ultimately have to be discarded when unacceptable toxicity or unavoidable side effects are encountered in later stages of development. However, this should not be surprising. Many side effects are usually closely related to the intrinsic receptor affinity responsible for the desired activity. In addition, closely related members of the gene family show significant drug promiscuity and, as a result of the generally similar function of these proteins, give rise to complex, composite clinical pharmacology. Hence, any sufficiently potent drug is likely to generate an array of, often undesired, biological responses. Finally, for most drugs, metabolism generates multiple metabolites that can have a qualitatively or quantitatively different type of biological activity, including enhanced toxicity, which further complicates the picture. Consequently, drug design should focus not on increasing activity alone, but on increasing the therapeutic index (TI), which reflects the degree of selectivity or margin of safety. TI is usually defined as the ratio between the median toxic dose (TD50 ) and the median effective dose (ED50 ) (or some other corresponding ratio): TI =

TD50 ED50

(3.1)

The current paradigm at most pharmaceutical companies is still to focus first on increasing the potency of the promising leads during the early drug discovery phase [the denominator of TI in eq. (3.1)], and the safety is evaluated only much later in the development phase [115]. However, as just discussed, this process is inherently inefficient because focusing efforts on potency does nothing to reduce toxicity; in fact, toxicity is likely to be increased without improving the therapeutic index. Hence, the likelihood of failure is not reduced for the new agent under development. Incorporate Metabolic and Targeting Considerations To fully estimate the potential for toxicity, one must also take into account that metabolic conversion of a drug (D) can generate multiple metabolites, including (1) analog metabolites (D1 , D2 , …) that have structures and activities similar to those of the original drug but have different pharmacokinetic properties; (2) other metabolites (M1 , M2 , …), including

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inactive ones (Mi ); and (3) potential reactive intermediates (I∗1 , I∗1 , …) that can be responsible for various types of cell damage by forming toxic species (for an example, see Figure 4-1). All these compounds are present simultaneously and at varying concentrations, as they tend to have different pharmacokinetic profiles; hence, the overall toxicity (T ) of any drug is, in fact, a combination of the intrinsic toxicity and selectivity of the original drug, Ti (D), and of all the toxicities due to the various metabolic products that are formed: T (D) = Ti (D) + T (D1 , D2 , …) + T (M1 , M2 , …) + T (I∗1 , I∗2 , …)

(3.2)

Accordingly, targeting and metabolism considerations should be an integral part of any drug design process to ensure that the desired NCE is designed with targeting and a preferred metabolic route in mind. The new molecule should be designed with site specificity and corresponding selectivity considerations already incorporated into its molecular structure. Site-specific delivery and action, if achievable and if sufficient for efficacy, might alleviate the undesired effects due to intrinsic toxicity. By designing and predicting the major metabolic pathways, the formation of undesired toxic, active, or high-energy intermediates might be avoidable. Surprisingly, the importance of early integration of metabolism, pharmacokinetic, and general physicochemical considerations in the drug design process has been clearly recognized in industrial settings only during the mid-1990s [55,56] despite numerous publications describing these concepts and methodologies in the early 1980s [116–122]. Notably, a recent analysis of 68 drugs, which have been recalled or associated with a black box warning due to idiosyncratic toxicity, and the top 200 U.S. drugs (based on prescription and sales in 2009) found that a significant proportion (ca. 80%) of drugs associated with toxicity contained structural alerts and that there was evidence indicating reactive metabolite formation as a causative factor for toxicity in approximately 70% of these molecules [123]. Along these lines, it is remarkable that even quite recently a review by a large group of experts, while recognizing the importance of drug metabolism in causing toxicity via reactive metabolites [124], instead of focusing on molecules that have “designed-in” metabolic pathways to avoid formation of reactive metabolites, still emphasized the importance of the detection and characterization of the respective (oxidative) metabolism and the identification of the metabolite–protein conjugates and their subsequent effects. They proposed various more or less sophisticated methods to detect the bioactivation of the drugs to chemically reactive metabolites that are likely to yield covalent binding, resulting in modified proteins, which have the potential to function as haptens, antigens, and immunogenes. Along these lines, the general concept of “structural alert” is used to warn of possible adverse drug reactions caused by reactive metabolites. Although there is no foolproof list of structural alerts, it may be wise to remove at-risk structures in the design and discovery phase. This is not possible in most cases, however, because currently only studies in humans can be used to discover mechanisms or adverse reactions and determine “cause and effect” with respect to bioactivation in humans and clinical outcome.

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The simplest solution, also mentioned in this review [124], is, of course, an avoidance strategy: eliminating chemical liabilities at the drug discovery phase either by appropriate candidate selection (chemicals without this liability) or by early chemical modifications. Although the best solution might be avoiding all currently known structural elements that could potentially cause adverse reactions, this is difficult to do, as we still cannot predict toxicity reliably on the basis of chemical structure alone. Hence, the second option (early chemical modification) is much more promising—assuming that it is done with the correct strategy. Such issues were discussed as early as 30 years ago (the Second IUPAC–IUPHAR Symposium in Noordwijkerhout, The Netherlands, August 25–28, 1981) in the dedicated session “Toxicological Parameters as a Lead” during a half-day debate between two different concepts: a “soft drug” approach, which was promoted by the senior author of this book and will be described here in detail, and a “hard drug” approach, which was promoted by E. J. Ari¨ens [125]. Along the principles that form the basis of this book, Bodor stated that a predictable in vivo destruction to nontoxic moieties is preferred. As part of this, it is also important to avoid most oxidative metabolism, which tends to result in highly reactive intermediates, and rely as much as possible on hydrolytic metabolism (e.g., those mediated by various esterases). While Ari¨ens also stressed the necessity of getting drug metabolism under control, he started from the viewpoint that “drug metabolism is drug waste.” Nonmetabolizable, highly active compounds have to be administered in very small quantities only. As Bodor pointed out, this would be desirable but virtually impossible to achieve because the body can metabolize almost everything and, in general, metabolism can be avoided only by going to pharmacokinetic extremes: either highly water-soluble drugs (e.g., cromolyn, i.e., cromoglicic acid), which essentially just run through the body, or highly lipophilic compounds, which accumulate in organelles. Therefore, the best solution still is to predict, design, and control the metabolic process. With time, this concept has evolved into the retrometabolic drug design approach, which is the main focus of this book.

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[88] Tufts Center for the Study of Drug Development. Outlook 2007, Tufts, Boston, MA, 2007, pp. 1–6. [89] Yildirim, M. A.; Goh, K. I.; Cusick, M. E.; Barabasi, A. L.; Vidal, M. Drug–target network. Nat. Biotechnol., 2007, 25, 1119–1126. [90] Scannell, J. W.; Blanckley, A.; Boldon, H.; Warrington, B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat. Rev. Drug Discov., 2012, 11, 191–200. [91] Walters, W. P.; Green, J.; Weiss, J. R.; Murcko, M. A. What do medicinal chemists actually make? A 50-year retrospective. J. Med. Chem., 2011, ePub. [92] Agnew, B. When pharma merges, R&D is the dowry. Science, 2000, 287, 1952–1953. [93] Kessel, M. The problems with today’s pharmaceutical business: an outsider’s view. Nat. Biotechnol., 2011, 29, 27–33. [94] Wolff, M. E. (Ed.) Burger’s Medicinal Chemistry and Drug Discovery, 5th ed., WileyInterscience: New York, 1995. [95] Munos, B. Lessons from 60 years of pharmaceutical innovation. Nat. Rev. Drug Discov., 2009, 8, 959–968. [96] Adams, C. P.; Brantner, V. V. Spending on new drug development. Health Econ., 2010, 19, 130–141. [97] DiMasi, J. A.; Hansen, R. W.; Grabowski, H. G. The price of innovation: new estimates of drug development costs. J. Health. Econ., 2003, 22, 151–185. [98] Moore, G. Cramming more components onto integrated circuits. Electronics, 1965, 38, April 19. [99] Grove, A. Rethinking clinical trials. Science, 2011, 333, 1679. [100] Borhani, D. W.; Butts, J. A. Rethinking clinical trials: biology’s mysteries. Science, 2011, 334, 1346–1347. [101] Giorgianni, S. J. (Ed.) The pharmaceutical industry at the start of a new century: unprecedented opportunity, unparalleled challenges. Pfizer J., 1997, 1(1), 1–37. [102] Avorn, J. Powerful Medicines: The Benefits, Risks, and Costs of Prescription Drugs, Knopf: New York, 2004. [103] Goozner, M. The $800 Million Pill: The Truth Behind the Cost of New Drugs, University of California Press: Berkeley, CA, 2004. [104] Ameringer, C. F. Chronic diseases and the high price of U.S. healthcare. Phi Kappa Phi Forum, 2012, 92, 4–6. [105] Dave, D.; Saffer, H. The impact of direct-to-consumer advertising on pharmaceutical prices and demand. NBER Working Paper 15969; National Bureau of Economic Research: Cambridge, MA, 2010. [106] Heylin, M. Drug and oil profits: How much is enough? Chem. Eng. News, 2006, 84, 76–76. [107] Horrobin, D. F. New drug discovery techniques are not so revolutionary. BMJ, 2001, 322, 239. [108] Kodadek, T. Rethinking screening. Nat. Chem. Biol., 2010, 6, 162–165. [109] Walker, M. J. A.; Barrett, T.; Guppy, L. J. Functional pharmacology: the drug discovery bottleneck? Drug Discov. Today, 2004, 3, 208–215. [110] Keiser, M. J.; Setola, V.; Irwin, J. J.; Laggner, C.; Abbas, A. I.; Hufeisen, S. J.; Jensen, N. H.; Kuijer, M. B.; Matos, R. C.; Tran, T. B.; Whaley, R.; Glennon, R. A.; Hert, J.;

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Thomas, K. L.; Edwards, D. D.; Shoichet, B. K.; Roth, B. L. Predicting new molecular targets for known drugs. Nature, 2009, 462, 175–181. DeWitte, R. S. On experimental design in drug discovery. Curr. Drug Discov., 2002, Feb. pp. 19–22. Dorsey, E. R.; Thompson, J. P.; Carrasco, M.; de Roulet, J.; Vitticore, P.; Nicholson, S.; Johnston, S. C.; Holloway, R. G.; Moses, H., 3rd. Financing of U.S. biomedical research and new drug approvals across therapeutic areas. PLoS One, 2009, 4, e7015. Knowles, J.; Gromo, G. Target selection in drug discovery. Nat. Rev. Drug Discov., 2003, 2, 63–69. Le Fanu, J. The Rise and Fall of Modern Medicine, Carrol & Graf: New York, 1999. Ulrich, R.; Friend, S. H. Toxicogenomics and drug discovery: Will new technologies help us produce better drugs? Nat. Rev. Drug Discov., 2002, 1, 84–88. Bodor, N. Novel approaches for the design of membrane transport properties of drugs. In Design of Biopharmaceutical Properties Through Prodrugs and Analogs; Roche, E. B., Ed.; Academy of Pharmaceutical Sciences: Washington, DC, 1977; pp. 98–135. Bodor, N. Soft drugs: strategies for design of safer drugs. In Strategy in Drug Research: Proceedings of the 2nd IUPAC–IUPHAR Symposium on Research, Noordwijkerhout, The Netherlands; Buisman, J. A. K., Ed.; Elsevier: Amsterdam, 1982; pp. 137–164. Bodor, N. Designing safer drugs based on the soft drug approach. Trends Pharmacol. Sci., 1982, 3, 53–56. Bodor, N. Novel approaches to the design of safer drugs: soft drugs and site-specific chemical delivery systems. Adv. Drug Res., 1984, 13, 255–331. Bodor, N. Soft drugs: principles and methods for the design of safe drugs. Med. Res. Rev., 1984, 3, 449–469. Bodor, N. The soft drug approach. Chemtech, 1984, 14(1), 28–38. Bodor, N. Soft drugs. In Encyclopedia of Human Biology; Dulbecco, R., Ed.; Academic Press: San Diego, CA, 1991; Vol. 7, pp. 101–117. Stepan, A. F.; Walker, D. P.; Bauman, J.; Price, D. A.; Baillie, T. A.; Kalgutkar, A. S.; Aleo, M. D. Structural alert/reactive metabolite concept as applied in medicinal chemistry to mitigate the risk of idiosyncratic drug toxicity: a perspective based on the critical examination of trends in the top 200 drugs marketed in the United States. Chem. Res. Toxicol., 2011, 24, 1345–1410. Park, B. K.; Boobis, A.; Clarke, S.; Goldring, C. E.; Jones, D.; Kenna, J. G.; Lambert, C.; Laverty, H. G.; Naisbitt, D. J.; Nelson, S.; Nicoll-Griffith, D. A.; Obach, R. S.; Routledge, P.; Smith, D. A.; Tweedie, D. J.; Vermeulen, N.; Williams, D. P.; Wilson, I. D.; Baillie, T. A. Managing the challenge of chemically reactive metabolites in drug development. Nat. Rev. Drug Discov., 2011, 10, 292–306. De Winter, M. L. Strategy in drug research. The second IUPAC–IUPHAR Symposium, held from 25 to 28 August 1981 in Noordwijkerhout, The Netherlands. Trends Pharmacol. Sci., 1982, 3, 132–134.

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Retrometabolic Drug Design DESIGN PRINCIPLES

Considering all the issues mentioned, it is a telling fact that two ideas formulated quite a while ago by two of the most important figures in medicinal chemistry still hold to a surprising degree. Successful drug discovery still relies on the same four G’s stated by Paul Ehrlich (in German): “Gl¨uck, Geduld, Geschick und Geld” (i.e., luck, patience, skill, and money). Sir James Black’s advice that “the most fruitful basis for the discovery of a new drug is to start with an old drug” is valid still to a surprising degree [1,2]. Retrometabolic drug design approaches provide general drug design strategies very much along the lines of the latter, as they usually start from a known lead structure and focus on designing safer, less toxic, and intrinsically better targeted drugs either through soft drug or chemical delivery system designs. Retrometabolic drug design approaches represent systematic methodologies that thoroughly integrate structure–activity and structure–metabolism relationships and are aimed at designing safe, locally active compounds with an improved therapeutic index [3–7]. They include two distinct methods (Figure 4-1). One approach is the design of soft drugs (SDs) [5,8–16], new, active therapeutic agents, often isosteric or isoelectronic analogs of a lead compound, with a chemical structure specifically designed to allow predictable metabolism into inactive metabolites after exerting their desired therapeutic effect(s). The other approach is the design of chemical delivery systems (CDSs) [5,17–24]. CDSs are biologically inert molecules intended to enhance drug delivery to a particular organ or site and requiring several conversion steps before releasing the active drug. Although both approaches involve chemical modifications of the molecular structure and both require enzymatic reactions to fulfill drug targeting, the principles of SD and CDS design are distinctly different. While CDSs are inactive as administered and sequential enzymatic reactions provide the differential distribution and ultimately release the active drug, SDs are active as administered and are designed to be easily metabolized into inactive species. Assuming an ideal situation, with a CDS the drug is present at the site and nowhere else in the body because enzymatic processes produce the drug only at the site, whereas with an SD the drug is present at the site and nowhere else in the body because enzymatic processes destroy the drug at those sites. Whereas CDSs are designed to achieve drug targeting at a selected organ or site,

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Mi inac
ve metabolite

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metabolism

I*1

I*2

I*3

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…

M2

M3

…

toxic metabolites

D1

D2

…

ac
ve metabolites

FIGURE 4-1. Retrometabolic drug design loop that includes chemical delivery system (CDS) design and soft drug (SD) design. Possible metabolic pathways for drugs (D) in general is included within the dashed box (see the text for details; cf. Figure 2-1). (See insert for color representation of the figure.)

SDs are designed to afford a differential distribution that can be regarded as reverse targeting.

4.2

TERMINOLOGY

Before discussing various aspects in detail, it might be useful to briefly clarify a few terminology-related issues. The retrometabolic designation has been introduced for these drug design approaches to emphasize that metabolic pathways are designed backward compared to the actual metabolic processes, in a manner somewhat similar to E. J. Corey’s retrosynthetic analysis [25], in which synthetic pathways are designed backward compared to actual synthetic laboratory operations. 4.2.1

Soft Drug vs. Hard Drug

The term soft drug was introduced in 1976 [8,26–28] to contrast Ari¨ens’s theoretical drug design concept of nonmetabolizable hard drugs. Hard drugs do not undergo any metabolism and hence avoid the problems caused by reactive intermediates or active metabolites. As mentioned earlier, metabolism can be avoided only by going to pharmacokinetic extremes: highly water-soluble drugs (e.g., cromolyn) that

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essentially just run through the body or highly lipophilic compounds that accumulate in organelles. For existing drugs, this is rarely the case; however, certain strongly lipophobic drugs, such as enalaprilat (the active metabolite of enalapril), lisinopril, cromolyn, and bisphophonates (e.g., alendronate), are essentially not metabolized in vivo and can be regarded as examples of hard drugs [29]. 4.2.2

Soft Drug vs. Prodrug

Unfortunately, soft drugs and prodrugs are still often confused; therefore, the difference between these two concepts should also be clarified [30]. Prodrugs are pharmacologically inactive compounds that result from transient chemical modifications of biologically active species [31–39]. The prodrug concept (including several specific examples) is discussed in more detail in Chapter 6 as an important brain-targeting strategy, but the main idea is that prodrug strategies are used because the structural requirements needed to elicit a desired pharmacological action and those needed to provide optimal delivery to the targeted receptor sites may not be the same. Hence, a chemical change is introduced to improve some deficient physicochemical property (e.g., membrane permeability, water solubility, chemical stability) or to overcome some other problem (e.g., rapid elimination, bad taste, formulation issues). It is important to note that before exerting its desired biological effect, a prodrug must undergo chemical or biochemical conversion to the active form. Therefore, in theoretical terms, the prodrug and the soft drug concepts are opposites: Whereas prodrugs are, ideally, inactive by design and are converted by a predictable mechanism to the active drug, soft drugs are active per se and are designed to undergo a predictable and controllable metabolic deactivation. Prodrugs and soft drugs tend to be confused mainly because both are designed to undergo predictable metabolic changes and both tend to rely primarily on enzymatic hydrolysis for these designed-in metabolic changes [40,41], which, however, as just mentioned, are activation for prodrugs but deactivation for soft drugs. Unfortunately, introduction of the antedrug terminology by Lee and Soliman in 1982 [42] only created additional confusion. The definition of an antedrug [42–46] is essentially the same as that of a soft drug [8,26–28], and it was introduced later. The Latin ante- prefix, which is very similar in meaning to the Greek pro- prefix (e.g., prior to, precedent), implies the conceptual opposite of a soft drug, that is, an inactive agent that has to be activated by metabolism. Hence, the antedrug terminology should be eliminated to avoid further confusion since as the authors themselves recognized it [44], prodrugs and antedrugs are “two diametrical approaches in designing safer drugs.” 4.2.3

Chemical Delivery System vs. Prodrug

Finally, the CDS concept evolved from the prodrug concept; however, it became essentially different by the introduction of targetor moieties and by the employment of multistep activation. Prodrug approaches may often be hampered by problems due to poor selectivity, poor retention, and the possibility for reactive metabolites,

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which may often end up decreasing the TI of drugs masked as prodrugs. Because of the introduction of additional complexity (e.g., chemical and/or enzymatic stability issues, the possibility of species-dependent metabolism, possible toxicity of the promoiety), prodrug approaches are worth considering whenever pharmacokinetically problematic moieties are an essential part of the molecule and are required for the desired biological activity [37], but, in general, they should be used only after careful consideration of all alternatives [36] even when used to improve oral bioavailability, one of the most popular uses of this strategy. CDSs were developed starting in the early 1980s to address such primarily site- or organ-targeting challenges [5,17– 23]. In addition to functional moieties, which are also contained by prodrugs to provide protected or enhanced overall delivery, CDSs also contain targetor moieties, responsible for targeting, site specificity, and lock-in. CDSs are designed to undergo sequential metabolic conversions, disengaging the modifier functions and, finally, the targetor, after this moiety fulfills its site- or organ-targeting role.

REFERENCES [1] Overington, J. P.; Al-Lazikani, B.; Hopkins, A. L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5, 993–996. [2] Lipinski, C.; Hopkins, A. Navigating chemical space for biology and medicine. Nature, 2004, 432, 855–861. [3] Bodor, N. Drug targeting and retrometabolic drug design approaches. Adv. Drug Deliv. Rev., 1994, 14, 157–166. [4] Bodor, N. Retrometabolic approaches to drug targeting. In Membranes and Barriers: Targeted Drug Delivery; Rapaka, R., Ed.; NIDA: Rockville, MD, 1995; NIDA Research Monograph Series, Vol. 154, pp. 1–27. [5] Bodor, N.; Buchwald, P. Drug targeting via retrometabolic approaches. Pharmacol. Ther., 1997, 76, 1–27. [6] Bodor, N.; Buchwald, P. Retrometabolism-based drug design and targeting. In Burger’s Medicinal Chemistry and Drug Discovery, Vol. 2, Drug Discovery and Drug Development, 6th ed.; Abraham, D. J., Ed.; Wiley: Hoboken, NJ, 2003; pp. 533–608. [7] Bodor, N.; Buchwald, P. Retrometabolic drug design: principles and recent developments. Pure Appl. Chem., 2008, 80, 1669–1682. [8] Bodor, N. Novel approaches for the design of membrane transport properties of drugs. In Design of Biopharmaceutical Properties Through Prodrugs and Analogs; Roche, E. B., Ed.; Academy of Pharmaceutical Sciences: Washington, DC, 1977; pp. 98–135. [9] Bodor, N. Soft drugs: strategies for design of safer drugs. In Strategy in Drug Research: Proceedings of the 2nd IUPAC–IUPHAR Symposium on Research, Noordwijkerhout, The Netherlands; Buisman, J. A. K., Ed.; Elsevier: Amsterdam, 1982; pp. 137–164. [10] Bodor, N. Designing safer drugs based on the soft drug approach. Trends Pharmacol. Sci., 1982, 3, 53–56. [11] Bodor, N. Novel approaches to the design of safer drugs: soft drugs and site-specific chemical delivery systems. Adv. Drug Res., 1984, 13, 255–331.

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[12] Bodor, N. Soft drugs: principles and methods for the design of safe drugs. Med. Res. Rev., 1984, 3, 449–469. [13] Bodor, N. The soft drug approach. Chemtech, 1984, 14(1), 28–38. [14] Bodor, N. Soft drugs. In Encyclopedia of Human Biology; Dulbecco, R., Ed.; Academic Press: San Diego, CA, 1991; Vol. 7, pp. 101–117. [15] Bodor, N.; Buchwald, P. Soft drug design: general principles and recent applications. Med. Res. Rev., 2000, 20, 58–101. [16] Bodor, N. Inactive metabolite approach to soft drug design. U.S. Patent 6,610,675, 2003. [17] Bodor, N.; Farag, H. H.; Brewster, M. E. Site-specific, sustained release of drugs to the brain. Science, 1981, 214, 1370–1372. [18] Bodor, N. Targeting of drugs to the brain. Methods Enzymol., 1985, 112, 381–396. [19] Bodor, N. Redox drug delivery systems for targeting drugs to the brain. Ann. N.Y. Acad. Sci., 1987, 507, 289–306. [20] Bodor, N.; Prokai, L. Site- and stereospecific ocular drug delivery by sequential enzymatic bioactivation. Pharm. Res., 1990, 7, 723–725. [21] Bodor, N.; Brewster, M. E. Chemical delivery systems. In Targeted Drug Delivery; Juliano, R. L., Ed.; Springer: Berlin, 1991; Handbook of Experimental Pharmacology, Vol. 100, pp. 231–284. [22] Bodor, N.; Buchwald, P. All in the mind. Chem. Br., 1998, 34(1), 36–40. [23] Bodor, N.; Buchwald, P. Recent advances in the brain targeting of neuropharmaceuticals by chemical delivery systems. Adv. Drug Deliv. Rev., 1999, 36, 229–254. [24] Bodor, N.; Buchwald, P. Targeting of neuropharmaceuticals by chemical delivery systems. In Blood–Brain Interfaces: From Ontogeny to Artificial Barriers; Dermietzel, R., Spray, D. C., Needergard, M., Eds.; Wiley-VCH: Weinheim, Germany, 2006; pp. 463–500. [25] Corey, E. J. Retrosynthetic thinking: essentials and examples. Chem. Soc. Rev., 1988, 17, 111–133. [26] Bodor, N.; Kaminski, J. J.; Selk, S. Soft drugs: 1. Labile quaternary ammonium salts as soft antimicrobials. J. Med. Chem., 1980, 23, 469–474. [27] Bodor, N.; Kaminski, J. J. Soft drugs: 2. Soft alkylating compounds as potential antitumor agents. J. Med. Chem., 1980, 23, 566–569. [28] Bodor, N.; Woods, R.; Raper, C.; Kearney, P.; Kaminski, J. Soft drugs: 3. A new class of anticholinergic agents. J. Med. Chem., 1980, 23, 474–480. [29] Lin, J. H.; Lu, A. Y. Role of pharmacokinetics and metabolism in drug discovery and development. Pharmacol. Rev., 1997, 49, 403–449. [30] Bodor, N. Prodrugs versus soft drugs. In Design of Prodrugs; Bundgaard, H., Ed.; Elsevier: Amsterdam, 1985; pp. 333–354. [31] Stella, V. J. Prodrugs: an overview and definition. In Prodrugs as Novel Drug Delivery Systems; Higuchi, T., Stella, V. J., Eds.; American Chemical Society: Washington, DC, 1975; pp. 1–115. [32] Bundgaard, H. (Ed.). Design of Prodrugs, Elsevier Science: Amsterdam, 1985. [33] Bodor, N.; Kaminski, J. J. Prodrugs and site-specific chemical delivery systems. Annu. Rep. Med. Chem., 1987, 22, 303–313. [34] Balant, L. P.; Doelker, E. Metabolic considerations in prodrug design. In Burger’s Medicinal Chemistry and Drug Discovery; Wolff, M. E., Ed.; Wiley-Interscience: New York, 1995; Vol. 1, pp. 949–982.

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[35] Stella, V. J. (Ed.). Themed issue: low molecular weight prodrugs. Adv. Drug Deliv. Rev., 1996, 19, 111–330. [36] Beaumont, K.; Webster, R.; Gardner, I.; Dack, K. Design of ester prodrugs to enhance oral absorption of poorly permeable compounds: challenges to the discovery scientist. Curr. Drug Metab., 2003, 4, 461–485. [37] Ettmayer, P.; Amidon, G. L.; Clement, B.; Testa, B. Lessons learned from marketed and investigational prodrugs. J. Med. Chem., 2004, 47, 2393–2404. [38] Wermuth, C. G.; Gaignault, J.-C.; Marchandeau, C. Designing prodrugs and bioprecursors. In The Practice of Medicinal Chemistry; Wermuth, C. G., Ed.; Academic Press: London, 2008; pp. 721–746. [39] Rautio, J.; Kumpulainen, H.; Heimbach, T.; Oliyai, R.; Oh, D.; J¨arvinen, T.; Savolainen, J. Prodrugs: design and clinical applications. Nat. Rev. Drug Discov., 2008, 7, 255–270. [40] Testa, B.; Mayer, J. M. Hydrolysis in Drug and Prodrug Metabolism, Wiley-VCH: Zurich, Switzerland, 2003. [41] Liederer, B. M.; Borchardt, R. T. Enzymes involved in the bioconversion of ester-based prodrugs. J. Pharm. Sci., 2006, 95, 1177–1195. [42] Lee, H. J.; Soliman, M. R. I. Anti-inflammatory steroids without pituitary-adrenal suppresion. Science, 1982, 215, 989–991. [43] Heiman, A. S.; Ko, D.-H.; Chen, M.; Lee, H. J. New steroidal anti-inflammatory antedrugs: methyl 3,20-dioxo-9a-fluoro-11␤,17a,21-trihydroxy-1,4-pregnadiene-16acarboxylate and methyl 21-acetyloxy-3,20-dioxo-11␤,17a-dihydroxy-9a-fluoro-1,4pregnadiene-16a-carboxylate. Steroids, 1997, 62, 491–499. [44] Lee, H. J.; Cooperwood, J. S.; You, Z.; Ko, D. H. Prodrug and antedrug: two diametrical approaches in designing safer drugs. Arch. Pharm. Res., 2002, 25, 111–136. [45] Khan, M. O.; Park, K. K.; Lee, H. J. Antedrugs: an approach to safer drugs. Curr. Med. Chem., 2005, 12, 2227–2239. [46] Khan, M. O. F.; Lee, H. J. Synthesis and pharmacology of anti-inflammatory steroidal antedrugs. Chem. Rev., 2008, 108, 5131–5145.

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Soft Drugs

Of the two retrometabolic drug design approaches, we discuss soft drug (SD) approaches first. As mentioned, soft drugs are new, active therapeutic agents, often isosteric or isoelectronic analogs of a lead compound, with a chemical structure specifically designed to allow predictable metabolism into inactive metabolites after exerting their desired therapeutic effect [1–11]. Accordingly, SDs are specifically designed by incorporating into their molecular structure specific structural elements, in addition to those required for activity, that are responsible for an optimized deactivation and detoxification route. In general, the desired activity is local, and the SD is applied or administered at or near the site of action. Therefore, in most cases, SDs produce pharmacological activity locally, but their distribution away from the site results in a prompt metabolic deactivation that prevents any type of undesired pharmacological activity or toxicity. Figure 5-1 provides a schematic illustration. In soft drug design, the goal is not to avoid metabolism, but rather to control and direct it. Inclusion of a metabolically sensitive moiety into the drug molecule makes possible the design and prediction of the major metabolic pathway and makes it possible to avoid the formation of undesired toxic, active, or high-energy intermediates. If possible, inactivation should take place as the result of a single low-energy high-capacity step that yields inactive species subject to rapid elimination. Most critical metabolic pathways are mediated by oxygenases. An analysis of the top 200 drugs found metabolism as the listed clearance mechanism for about 75% of them, and it was predominantly oxidative; of the drugs cleared via metabolism, about 75% are metabolized by cytochrome P450 (CYP) enzymes, mainly CYP3A, CYP2C9, CYP2C19, CYP2D6, and CYP1A (Figure 2-4) [12,13]. Because oxygenases exhibit not only interspecies but also interindividual variability and are subject to inhibition and induction [14], and because the rates of hepatic monooxygenase reactions are at least two orders of magnitude lower than the slowest of the other enzymatic reactions [15], it is usually desirable to avoid oxidative pathways as well as these slow, easily saturable oxidases. Accordingly, the design of soft drugs should be based on moieties inactivated by hydrolytic enzymes. Rapid metabolism can be carried out more reliably by ubiquitously distributed esterases. Not relying exclusively on metabolism or clearance by organs such as liver or kidney is an additional advantage because blood flow and enzyme activity in these organs can be seriously impaired in critically ill patients.

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SOFT DRUGS

D

Mi *

I1

M1 SD

D

Mi

I*2 M2 Mi

D Tradional drug

So
drug

FIGURE 5-1. Difference between traditional drugs (D) and soft drugs (SD) for the case of ocular administration. For a traditional drug, a significant portion of the dose administered reaches the systemic circulation, whereas for a soft drug, the designed-in metabolism, which generates an inactive metabolite Mi , rapidly deactivates any fraction that might reach the systemic circulation; hence, the local effect is accompanied by no or just minimal side effects. (See insert for color representation of the figure.)

5.1

ENZYMATIC HYDROLYSIS

Since SD approaches rely overwhelmingly on hydrolysis for metabolic deactivation, we discuss briefly some relevant aspects. Hydrolysis, as its name implies, indicates the chemical or metabolic change of a bond by the addition of water. The major functional groups that are subject to hydrolysis are esters, amides, and epoxides. Among the groups that are of interest from a drug design perspective, the relative ease of the hydrolysis is generally thioesters > esters > amides, and the corresponding products are a carboxylic acid and a thiol, alcohol, and amine, respectively [16]. Amides tend to be more difficult to hydrolyze than esters because the lone electron pair on the nitrogen is delocalized, giving the bond between the nitrogen and the carbonyl carbon a bond order greater than 1, which makes this more than a single bond and also makes the amide nitrogen not basic. The oxygen atom in the esters is more electronegative; hence, the nonbonded electrons of oxygen are less prone to delocalization than are those of the nitrogen. The same enzyme can often hydrolyze esters, thioesters, and amides; therefore, the differentiation between esterases and amidases is somewhat artificial. Esterases that contribute to human drug metabolism fall into three major classes: the cholinesterases (acetylcholinesterase, pseudocholinesterase, butyrylcholinesterase, etc.), carboxylesterase, and paraoxonase [16,17]. A detailed and comprehensive review on the hydrolysis of various drugs and prodrugs has been published relatively recently by Testa and Mayer [17].

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Esterases

Most carboxylic ester–containing chemicals are hydrolyzed very efficiently into the respective free acids by a class of enzyme designated as carboxylic ester hydrolases (EC 3.1.1). Unfortunately, because these widely occurring enzymes exhibit broad and overlapping substrate specificity toward esters and amides, and because, in many cases, their exact physiological role remains unclear, their classification is difficult [18–24]. Intriguingly, in many cases, the physiological role of these enzymes remains unclear [23]. Some members of the esterase family are specialized for extreme rapidity, such as acetylcholinesterase (which has kinetics close to the theoretical maximum); some are specialized for high substrate specificity, such as certain hormone esterases; and some might be specialized for versatility, such as (nonspecific) carboxylesterases [25]. According to an older but still used classification system [26], the more important subclasses include carboxylesterase, EC 3.1.1.1 (carboxylic-ester hydrolase, ali-esterase, B-esterase, monobutyrase, cocaine esterase, etc.); arylesterase, EC 3.1.1.2 (A-esterase, paraoxonase); acetylcholinesterase, EC 3.1.1.7 (choline esterase I); and cholinesterase, EC 3.1.1.8 (choline esterase II, pseudocholinesterase, butyrylcholine esterase, benzoylcholinesterase, etc.). A possible solution to the existing confusion might come from the accumulation of adequate sequence information and the emergence of a novel classification system based on this information [24]. According to the phylogenetic alignment of genes from several species, mammalian carboxylesterase isozymes are now classified into five major classes, CES1 to CES5 [24,27]. They are expressed ubiquitously with high levels in various tissues, with the highest activity typically found in the liver and other tissues, such as testis, kidney, and plasma [27]. There are two major human carboxylesterases, hCE-1 (CES1A1) and hCE-2 (CES2) [24,28], and a more recently reported third isoenzyme, CES3 [29]. There seems to be some separation in their activity, with hCE-1 preferentially hydrolyzing substrates with relatively smaller alcohol parts compared to the acyl part (e.g., cocaine methyl ester, delapril, meperidine), and hCE-2 preferentially hydrolyzing substrates with relatively bulkier alcohol parts as compared to the acyl part (e.g., cocaine benzoyl ester, heroin, irinotecan) [28,30]. Both hCE-1 and hCE-2 are highly expressed in the liver, and hCE-1 seems more important for clearance through the kidney, whereas hCE-2 seems more important for the clearance of orally administered drugs through the small intestine and colon [28]. Esterases belong to a superfamily of ␣/␤-fold proteins, which contain a similar core of ␤-sheets connected by ␣-helices. Esterases have widely varying sequence identities but share similarities in secondary and tertiary structures and are related by divergent evolution [25,31]. They also rely on a similar mechanism (Figure 5-2) centered on a catalytic triad, of which the most common set is Ser–His–Glu (or Asp) and that seems to lie at the base of a deep catalytic gorge (Figure 5-3). From our present main perspective of retrometabolic drug design, a brief review of some relevant aspects is important since these strategies, together with those related to prodrug design, rely primarily on enzymatic hydrolysis for drug activation (prodrugs, chemical delivery systems) or deactivation (soft drugs). Despite their metabolic sensitivity, ester structures are of sufficient chemical stability to provide the shelf life necessary for marketable drugs.

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FIGURE 5-2. Mechanism proposed for hydrolysis by carboxylesterases based on analogy with similar mechanisms and a study of highly conserved motifs [24]. It involves Ser203 , Glu335 , and His448 as a catalytic triad and Gly123 –Gly124 as part of an oxyanion hole, and it also agrees well with the QSMR observations from our studies [33,62], as discussed in the text.

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FIGURE 5-3. Active site of an esterase using the crystal structure of human carboxylesterase 1 (hCE1) in a complex with the cocaine analog homatropine (shown as a darker ball-and-stick structure) prepared using PDB structure 1MX5 [66]. Here, the catalytic triad (Ser221 , Glu354 , and His468 ), which resides at the base of the substrate-binding gorge, is aligned in a manner typical of serine esterases, and the oxyanion hole is defined by Gly142 and Gly143 . (See insert for color representation of the figure.)

5.1.2

Interspecies Variability

Unfortunately, esterase activity not only depends on the substrate but also shows strong interspecies, interindividual, and interorgan variability [18,24,27,32–35]. There is strong evidence that esterase activity varies considerably between species [18–24,27,35,36], and, as extreme examples, even compounds that are metabolized in one species but not in others are known. For example, atropine is not hydrolyzed in human serum but is hydrolyzed in the serum of some rabbits [37]. Aliphatic esters tend to be metabolized much faster by rodents (rats, guinea pigs) than by humans; several examples, including clevidipine, esmolol, isocarbacyclin methyl ester TEI9090, remifentanil, and soft cannabinoid analogs, are discussed in more detail later (Figure 5-4) [32,33,38–43]. Aromatic esters, however, might show an opposite trend, as exemplified by flestolol [44] and by nicotinate esters [45]. Therefore, compared to the usual problems related to the extrapolation of animal test results to humans [46, 47], preclinical evaluation of ester-based SDs might be even more challenging and animal data less predictive of human clinical trial results. Contrary to the two most frequently used animal models—rodents, which, in general, tend to hydrolyze faster than humans, and dogs, which, in general, tend to hydrolyze slower than humans (at least for aliphatic esters) [33,43]—monkeys might serve as better models for the

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FIGURE 5-4. Interspecies variability of hydrolytic half-lives, illustrated by rat vs. human in vitro blood data [34] for the structures indicated (clevidipine, esmolol, isocarbacyclin methyl ester TEI-9090, remifentanil, and the aromatic ester-containing flestolol).

hydrolytic metabolism of humans [32,48]. On the other hand, our ability to predict PK in humans from animal studies is not very good in general, not just for esters. It might be somewhat better than “pure luck” (as described by Brodie in the 1960s, due to the great differences in metabolizing enzymes found throughout various species—cited in [49]), but experimental animals are not very good predictors of human oral bioavailability [50] or human PK parameters (e.g., clearance) in general [51,52]. 5.1.3

Interorgan and Interindividual Variability

Carboxylesterases are expressed in a variety of organs and tissues. For example, humans have been shown to express carboxylesterase in the liver, plasma, small intestine, brain, stomach, colon, macrophage, and monocytes [24,27]. Nevertheless, esterase activity is known to vary strongly between organs and tissues. In fact, for the activity of a nonspecific steroidal esterase, which was measured by two different assays, the interorgan variability observed was considerably larger than the interspecies variability observed [53]. Not unexpectedly, in all cases examined (human, rat, and mouse), liver gave the highest activity. If sufficiently tissue-specific esterase activity could be identified, retrometabolic or prodrug approaches can provide excellent means for tissue-specific drug delivery. Interindividual variability and pharmacogenetics can also cause variability. For the above-mentioned nonspecific steroidal esterase, considerable (approximately 18-fold) interindividual variability was observed in human mammary tissues from 16 healthy female subjects. As an intriguing aspect, this esterase activity showed a statistically significant age-related increase [53]. Even larger, 5- to 45-fold and

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substrate-dependent interindividual variability was found in carboxylesterase activity in human liver microsomes from 12 different donors for 10 different substrates [54]. Plasma hydrolytic activity among 20 patients showed a smaller, only about three- to five fold variability in irinotecan, p-nitrophenyl acetate, and butyrylthiocholine hydrolysis [55]. Human liver activity also showed a similar, up to five- to six fold variability for the hydrolysis of phenylvalerate and some paraben esters [56]. Polymorphic rates of ester hydrolysis in New Zealand white rabbit blood and cornea were found for the metabolism of flestolol and other esters [44]. About 30% of the animals studied were found as “slow” metabolizing and about 70% were found as “fast” metabolizing. However, no bimodal distribution was found in blood from rats, dogs, and humans or in the aqueous humor and iris–ciliary body complex of rabbits [44]. For humans, genetic variants are known for butyrylcholinesterase and arylesterase. Atypical butyrylcholinesterase occurs in homozygous form in approximately 1 of 3500 Caucasians. As a result of impaired ester hydrolysis, these patients exhibit prolonged paralysis after standard doses of neuromuscular blocking agents (e.g., succinylcholine, suxamethonium, mivacurium) (see [57] and references therein). This is unlikely to be a significant issue for soft drugs, and some evidence has been established in a few cases. For example, in a small number of subjects studied, the in vitro half-life of clevidipine increased by only about 50% [42]. Also, the half-life and clearance of AZD3043 were unaffected by the coadministration of butyrylcholinesterase substrate drugs, making it unlikely to be subject to in vivo PK interactions when coadministered with other drugs metabolized by the same enzyme, such as remifentanil and succinylcholine [58]. 5.1.4

Mechanism: Catalytic Triad and Oxyanion Hole

As mentioned, it seems that hydrolysis by carboxylesterase involves a catalytic triad formed by a serine, a histidine, and either a glutamate or an aspartate residue, so that low-barrier hydrogen bonds facilitate a general base mechanism for the acylation of serine (Figure 5-2) [24,59]. Sequences required for hydrolytic capability at the catalytic triad seem to be highly conserved in carboxylesterase, acetylcholinesterase, butyrylcholinesterase, and cholesterol esterase [24]. Two glycine residues also play important roles as parts of an oxyanion hole in which weak hydrogen bonds stabilize the tetrahedral adduct (Figures 5-2 and 5-3). The important rate-influencing role of these glycine residues has been confirmed by site-directed mutagenesis studies in human acetylcholinesterase [60]. A stabilizing role for the hydrogen bonds agrees well with the observations of Page and co-workers [61]. For alkaline hydrolysis, they found that electron-withdrawing substituents increase the rate, and the corresponding Brønsted exponents indicate a transition state that resembles an anionic tetrahedral intermediate with a localized negative charge. By contrast, for enzymatic hydrolysis (by pig liver esterase), they found little dependence on the electron-withdrawing power of substituents, which is consistent with a transition state that resembles a neutral tetrahedral intermediate [61]. Our own quantitative structure–metabolism relationship study [62], discussed briefly later, found the rate of enzymatic hydrolysis to strongly correlate with the

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steric hindrance of the carbonyl sp2 oxygen. By contrast, chemical hydrolysis rates correlated more strongly with the steric hindrance of the reaction center, the carbonyl carbon. This also suggests an important, possibly even rate-determining role for the hydrogen bonds formed in the oxyanion hole, as they can both make the sp2 carbon more susceptible toward a nucleophilic attack and stabilize the tetrahedral adduct by a partial proton transfer [62]. The important role of these hydrogen bonds could explain both a more neutral transition state and the importance of the steric hindrance around the oxygen atom. It can also provide an explanation for the long-known observation (e.g., [63]) that chemical hydrolysis rates tend to afford just low correlations with enzymatic hydrolysis rates. Experimental and modeling data seem to indicate that the active-site serine residue lies at the bottom of an approximately 25-Å-deep gorge that is negatively charged and relatively narrow (3 to 4 Å diameter) [59,64]. This can explain why negatively charged (e.g., carboxylic acid–substituted) compounds are poor substrates, which is expected because carboxylic acids are products of hydrolysis and should be repelled from the active site. It might also explain why, in certain cases, relatively small increases in size can completely limit access to the active site and hence hydrolysis. An interesting and probably related switch with increasing size occurs for alkanedicarboxylic diesters −(CH2 )n − −CO2 Me. Whereas for shorter chains (n < 8), hydrolysis such as MeO2 C− (by porcine liver esterase) produces the pure monoester exclusively, for longer chains (n ≥ 8), it produces no monoester at all, yielding the diacid or the untransformed diester in about a 50%–50% proportion [65]. On the other hand, the active site of hCE1 seems to contain both a small rigid pocket, which accommodates the ester moiety, and a large, flexible region, which can accommodate structurally diverse side chains, resulting in its wide substrate specificity [66,67]. 5.1.5

Kinetics

A number of investigators found enzymatic hydrolysis reactions to obey Michaelis– Menten kinetics [45,61,68–71], meaning that the rate of product formation, v = dP/dt, is connected to substrate concentration (C = [S]) through the well-known relationship [discussed briefly in Section 2.2.3, see eq. (2.22)] and characterized by a maximum velocity V max , which is determined by kcat and total enzyme concentration E0 (V max = kcat E0 ), and a Michaelis constant K MM , which corresponds to the concentration at half-maximal velocity (C = K MM ⇒ v = V max /2) [72,73]: v = Vmax

C C + K MM

(5.1)

However, under most pharmaceutically relevant conditions, the substrate concentration is sufficiently low (C  K MM ) that these reactions can be considered as pseudo–first-order and characterized by a constant rate, k = V max /K MM : v=−

dC Vmax = kC = C dt K MM

(C  K MM )

(5.2)

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Under such conditions, degradation of the substrate is described by the exponential equation [corresponding to the solution of eq. (5.2)] C = C0 e−kt

(5.3)

Accordingly, the degradation shows good linearity on a semilog scale (log concentration vs. time) and can be characterized by the rate constant k or the corresponding half-life, t1/2 = 0.693/k, as discussed briefly in Section 2.2.3 [see eq. (2.18)]. The efficiency of these enzymatic reactions can be well illustrated by the enzyme rateenhancement factor (EREF) concept introduced by Page and co-workers [61,74]. This represents the ratio between the second-order rate constant for the enzyme-catalyzed reaction kcat /K m and that for the hydrolysis of the same substrate catalyzed by hydroxide ion, kOH . For hydrolytic enzymes, EREFs are large values, usually within the range 103 to 107 [61,74]. 5.1.6

Stereoselectivity

Within the pharmaceutical field, only relatively limited data are available on the stereoselectivity of enzymatic hydrolysis, despite this being an important aspect of enzyme-catalyzed reactions. Nevertheless, stereoselective hydrolysis has been documented in various media for a number of cases, such as ester prodrugs of oxazepam [75,76], propranolol [77–80], and ibuprofen [81]. In the last case, R/S rate ratios as high as 50 were reported. Cocaine hydrolysis in baboon plasma provides an even more extreme example. The behaviorally inactive (+)-cocaine was found to hydrolyze at least 1000 times faster than (−)-cocaine, the naturally occurring enantiomer [82]. Cocaine hydrolysis in rat hepatocytes also showed considerable stereoselectivity [83]. Flurbiprofen derivatives were R-preferentially hydrolyzed in the liver microsomes of various animal species, but barely hydrolyzed in the small intestine microsomes of any species except rabbit [35]. Elucidation of the rationale behind the enantioselectivity of certain hydrolytic enzymes may become possible with the accumulation of data on the mechanism of various enzymatic hydrolysis (see, e.g., [84] and references therein). 5.1.7

Activation Energy and Temperature Dependence

Data on the temperature dependence of the rate of hydrolysis in human blood are available for esmolol [85] and clevidipine [42] (for the corresponding chemical structures see 5-16 and 5-223 in Figures 5-9 and 5-65, respectively). With the decrease in media (blood) temperature, the rate (k) decreases and hence the half-life increases. For both compounds, but especially for clevidipine, good linearity was observed between the logarithm of the rate (or the half-life) and the inverse of the absolute temperature (1/T) as required by an Arrhenius-type equation [86]: k = Ae−Ea /RT

(5.4)

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From the corresponding slopes, we have obtained apparent activation energies Ea of 55.5 kJ/mol (n = 4, r2 = 0.9586) and 76.6 kJ/mol (n = 3, r2 = 0.9998) for esmolol and clevidipine, respectively [34]. This is in reasonable agreement, for example, with the activation energy of about 75 kJ/mol found for lactose hydrolysis by recombinant ␤-glycosidases [87] or that of 43.5 kJ/mol found for anandamide hydrolysis by human brain fatty acid amide hydrolase [88]. On the basis of data collected by Charton [89,90], we have also estimated activation energies for the chemical hydrolysis of a series of simple esters under acidic or basic catalyzes, and obtained values that were also in the range 40 to 70 kJ/mol. Values for the chemical hydrolysis of liposomal phosphatidylcholine under acidic conditions (pH 4.0) were also mostly in the range 60 to 70 kJ/mol [91]. Extrapolation of the above-mentioned in vitro enzymatic hydrolysis results for ester-containing drugs such as esmolol and clevidipine to in vivo situations has to be done carefully because a number of other effects are also temperature dependent. Nevertheless, considering the Ea values obtained here, the data suggest that a reduction in body temperature from 37◦ C to around 30◦ C, which is done routinely during cardiac surgery, may approximately double the half-life of such drugs [42]. Indeed, pharmacokinetic studies in hypothermic and normothermic patients with remifentanil and clevidipine showed prolonged half-life and reduced clearance at lower temperatures [42,92]. 5.1.8

Structure–Metabolism Relationships

Many pharmaceuticals, usually members of some prodrug or soft drug series, have been investigated to establish the effect of structure on hydrolytic half-life (see [33,93] and references therein). It was obvious from the beginning that increasing steric hindrance, such as that produced by branched substituents, increases half-life, but useful quantitative structure–metabolism relationships (QSMRs) of general validity proved difficult to obtain. One has to emphasize that the ability to introduce rigorously measurable, quantitative aspects into structure–activity, structure–property, or similar types of relationships is one of the most important advancements in medicinal chemistry and drug design [94–99]. QSAR/QSMR Modeling: Background As reviewed in earlier chapters, the physiological effects generated by drugs (and biologically active substances in general) are, in the end, a function of (1) the amount of active compound that actually reaches the receptor (or an effect compartment in general) and (2) the strength of the interaction at this site (affinity) plus the relevance of the structural changes produced at this site (efficacy). Because, ultimately, all these are determined by intermolecular forces, the main determinants of the biological activity of a compound are its physicochemical properties and its relevant structural features. These, in fact, also represent the basic assumptions underlying any quantitative structure–property, structure–activity, structure–metabolism relationship (QSPR, QSAR, QSMR) studies [100]. Such QSPR/QSAR/QSMR studies attempt to formulate the connection between chemical structure and some desired biological (or physicochemical)

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property and/or activity in mathematical form (i.e., in rigorously quantifiable terms) [95,96,98–103]. The chem-bioinformatics designation introduced in the early 2000s by Hansch and co-workers was intended to cover essentially the same subject area: to understand chemical–biological interactions in mathematical terms [104, 105]. From a pharmaceutical/drug design perspective, the ultimate goal is to describe physiological activity as a function of chemical structure in a quantitative manner: activity = f (structure)

(5.5)

It is important to remember that because distribution, transformation, and binding processes are essentially equilibrium processes characterized by equilibrium constants K governed by the corresponding free-energy differences (
G) as described by eq. (2.3), such relationships should use logarithmic scale and adequate biological or chemical activity and property data (e.g., in vitro or in vivo activities measured by reciprocal molar concentrations 1/C, such as median effective or lethal doses, 1/ED50 , 1/LD50 ; substrate or receptor binding constants Kd ; rate constants k; inhibition constants Ki ; or pharmacokinetic parameters, e.g., kel ). The logarithmic scale also ensures normal distribution of the experimental error of biological tests, a requirement for adequate statistical analyses, including linear regression. Therefore, the ultimate QSAR goal [eq. (55)] is most commonly formulated in terms of finding such suitable structure-related ␰ i descriptors that the activity, quantified as some log 1/C type of data, can be written as a parametrizable function of these descriptors: log 1/C = f (␰1 , ␰2 , …)

(5.6)

Unfortunately, the observable final biological responses are usually a function of many interacting, overlapping, or competing processes from absorption, distribution, metabolism, and elimination (ADME) to specific or nonspecific binding, bindinginduced structural changes, and the presence of fine-tuned compensating biological mechanisms (Figure 2-1). Hence, a clear overall picture rarely emerges. Nevertheless, general guidelines for properties influencing distribution and related processes as well as structural requirements for binding to certain well-defined molecular targets can often be derived. Because elimination of any unlikely candidate from chemical synthesis and from in vitro/in vivo testing provides significant time and financial savings, the development of reliable computerized (in silico) QSPR/QSAR/QSMR models or screening filters can afford considerable benefits. For limited groups of compounds, with suitable descriptors and over limited ranges, QSPR/QSAR/QSMR data are often well described by linear functions, and they tend to be applied because of their unrivaled simplicity and intuitive and convenient interpretation. By writing these functions in a general form as log 1/C = a0 +


i

ai ␰i

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they can incorporate in a single form the two most widely used QSAR approaches: the extrathermodynamic (linear free-energy relationship, LFER, or Hansch) approach [99,101] if the ␰ i represent properties used as descriptors in this interproperty relationship equation (e.g., log P, ␴, Es ), and the Free–Wilson approach [106] if the ␰ i represent indicator (counting) variables for fragment i (e.g., I OH , I COOH , I NH2 ). The latter, also called the additivity model or de novo approach, was proposed in an early form by Bruice and co-workers [107], but it is used almost exclusively only as modified by Fujita and Ban [108]. Equation (5.7) obviously also incorporates the mixed model, which is a combination of these two [99]. In essence, QSAR-type approaches, summarized in eq. (5.7), are based on the assumption that having a sufficient number of adequate descriptors (parameters and/or available properties), the value of other properties or activities of interest can be predicted. In a geometric interpretation, this requires the data points representing the chemical compounds in the corresponding multidimensional parameter space to be distributed in a subspace of fewer dimensions. This geometric interpretation can be visualized only for the two-dimensional (one property, one descriptor) and three-dimensional (one property, two descriptors) cases. Such visualization is also useful in interpreting principal component analysis (PCA) [109], a method used frequently when analyzing large amounts of data. Principal components represent new (mutually orthogonal) axes through the points representing the chemical compounds in n-dimensional space, corresponding to the available n properties. The first principal component describes the best line through all points and corresponds to the longest dimension. It is some linear combination of the original n properties and accounts for the largest possible part of the variance in the data. The second principal component is orthogonal to the first and corresponds to the next longest dimension in the orthogonal direction. PCA can be useful if only the first few such principal components can account for most of the existing variance in the data. Typically, the coefficients ai of eq. (5.7) are determined by multiple linear regression analysis. This is not only convenient because linear regression approaches are well known, relatively nonsophisticated, and commonly applied but also because the resulting linear functions allow easy interpretability (a main advantage over “black box” approaches such as neural networks [110], which have been introduced more recently in QSAR and drug design studies [111,112]). Undeniably, the use of linear functions is also often theoretically justifiable, especially over a relatively narrow range of the descriptor where even different functional dependencies can be well approximated by linear functionalities that provide unrivaled simplicity. Over a wider range, however, data often show an extreme value (i.e., maximum or minimum), and therefore can no longer be described adequately by linear functions. Parabolic functions advocated by Hansch and co-workers [101,113,114], such as the often used prototypical LFER equation log 1/C = ␣0 + ␣1 log P + ␣2 (log P)2 + ␣3 Es + ␣4 ␴ + …

(5.8)

can provide convenient functionalities that are suitable for regression-type analyses, but they are entirely empirical, can only fit symmetrically rising and descending data,

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deviate from the linearity seen over more limited ranges, and are difficult to harmonize with the linear functions eventually used on these limited ranges. To overcome these difficulties, we have recently introduced a general linearized biexponential (LinBiExp) model that was obtained starting from assumptions corresponding to very general physicochemical considerations and by using a differential equationbased approach, and are discussed later in the context of specific applications [see eq. (5.10)]. In general, quantitative structure–property relationships (QSPRs) tend to be the easiest to obtain starting from chemical structure, as “property” in its strictest and most commonly used sense designates only physicochemical properties; hence, no biological systems are involved. The basic physicochemical properties that are of special interest for drug design include, for example, boiling points, melting points, vapor pressure, acidity/basicity (pKi ), lipophilicity (most frequently measured by the log octanol/water partition coefficient log Po/w ), aqueous solubility, critical micelle concentration, cyclodextrin complexation energies, and others. Some of these can be predicted relatively reliably for most compounds of pharmaceutical interest; comprehensive reviews have been published [115,116]. For obvious reasons, lipophilicity and aqueous solubility received particular interest; they are discussed in more detail later, as their modeling is also integrated into the computer-aided soft drug expert system. In a wider sense, QSPR also covers biological “properties” [117] such as biomembrane permeabilities, including Caco-2 [118,119], blood–brain barrier [120], intestinal [121], corneal [122], or skin [123] permeability. It can even include complex properties such as intestinal drug absorption and bioavailability [124–131], and there is no denying a recent strong and fast-expanding progress in the field of in silico modeling of ADMET properties [132]. In fact, in a widest sense, any activity, affinity, or toxicity is a property, but for classification purposes, it is probably a useful separation to reserve the QSPR term for properties relevant for the pharmaceutical and pharmacokinetic phases (i.e., required to reach the desired target) and the QSAR term for properties more relevant for the pharmacodynamic phase (i.e., required to produce the desired activity at the target)—to the extent that such properties are separable. Historically, QSAR (quantitative structure–activity relationship) was the oldest term. The origins of the field are usually traced back to a 1962 paper by Hansch and coworkers [133], but the terms quantitative structure–activity, structure–property, and structure–metabolism relationship are first mentioned as such in the Thomson Reuters ISI database in 1967 [134], 1977 [135], and 1980 [136], respectively. Thousands of classical and less classical QSARs have already been established (see [99,105] and references therein). Many of them, however, have to be treated cautiously because they were derived on congener or closely related structures and based on a relatively small number of data. Therefore, molecular descriptors might be intercorrelated, the data points/variables ratio might be low, and their conclusions might not be generalized to other structures. Despite these and the many limitations inherent in such quantitative models (“All models are wrong, but some are useful”—G. E. P. Box), they contributed significantly to our present knowledge of relationships between chemical structure and chemical or biological activity [94–96,98,99,137,138]. The main contribution of

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QSAR approaches is not to extrapolate and pinpoint highly active new structures, which they are highly unlikely to do, but to slowly transform the field of medicinal chemistry into a rigorous science. Even if often formulated only in hindsight, the ability to attach a quantitative measure to a model is a significant step. As noted above, quantitative structure–metabolism relationship (QSMR) analysis is a relative newcomer to the field of quantitative analysis. The fact that metabolism is possibly the most complicated of the four ADME processes that determine PK characteristics, because of the variety of enzymes involved, is certainly part of the reason. A number of the latest applications as well as computer systems for the prediction of xenobiotic metabolism have been reviewed fairly recently [139,140]. Understandably, for (quantitative) metabolism studies, cytochrome P450 (CYP) and its various isoforms are of special interest, as they mediate most of the phase I metabolism [141]. They have been the subject of various approaches, including classical LFER for CYP3A4 [142], CoMFA for CYP2C9 [143], and combined protein and pharmacophore model for CYP2D6 [144,145] (see [141] and references therein for details). Our main interest here is in the modeling of esterase-mediated hydrolytic metabolism. Solid-Angle-Based QSMR Model QSMR attempts for the hydrolysis of individual ester-containing series were made by Testa and co-workers [45,68,146,147], by Charton [148], and by Altomare and co-workers [149]. However, such studies can only provide general guidelines and no quantitative predictions for other, noncongener series. We have identified a more general relationship on the basis of human blood in vitro metabolism data of more than 80 compounds belonging to seven different classes [62] and including the ␤-blocker series with ultrashort duration of action, ultrashort-acting ACE inhibitors, opioid analgetics, soft corticosteroids, short-acting antiarrhythmic agents, and buprenorphine prodrugs. In vitro human blood data were used because they represented the data of interest available in the largest number over the widest range of structures under comparable experimental conditions. Also, such data are of special interest for us, as extrahepatic metabolism is expected to play a major role in the deactivation of soft drugs. The predictive power of the model has been tested on five separate ester-containing drugs with completely unrelated structures: vinyl acetate, isocarbacyclin methyl ester (TEI-9090), glycovir, clevidipine, and itrocinonide [33,62]. Following our work, Bianucci and co-workers published a binary (yes/no) classification QSAR model using in vitro human plasma data from close to 200 compounds [150]. In general agreement with previous results, we found steric effects as having the most important influence on the rate of enzymatic hydrolysis. Lipophilicity, as measured by the QLogP calculated log octanol/water partition coefficients and some of the electronic parameters, such as the charge on the carbonyl C (qC= ), also proved informative, but to a much lesser degree. Half-lives were found to increase with increasing steric hindrance around the ester moiety, as measured by a measure of three-dimensional inaccessibility parameter, the inaccessible solid angle h (Figure 5-5). An important novelty was the finding that the rate of metabolism as measured by log t1/2 seems to be more strongly correlated with the steric

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FIGURE 5-5. Three-dimensional views of the inaccessible solid angle h . The hindered access around a selected atom by a 1-chloro-2-hydroxyethyl substituent (with the Cl in the back) is shown. The atom for which the hindered, inaccessible solid angle is illustrated is represented at the top of both figures as a transparent “glass” sphere (left) and a transparent sphere with a small, white center (right), respectively. The area of the shadow projected by the light placed in the center of the atom selected on a circumscribing sphere is proportional with h . For transparency, this atom was made of “glass,” which also causes a distortion of the light rays passing through it (top of the left-hand figure). (See insert for color representation of the figure.)

2 hindrance of the carbonyl sp2 oxygen (O= h : r = 0.58, n = 79) than with that of the C= 2 2 carbonyl sp carbon as measured by (h : r = 0.29). As mentioned, this seems to provide evidence for the important, possibly even rate-determining role played by hydrogen bonding at this oxygen atom in the mechanism of this reaction. We settled on a final equation [eq. (5.9)] to estimate log t1/2 , which in addition includes the AM1-calculated charge on the carbonyl carbon (qC= ) and a to O= h calculated log octanol/water partition coefficient (QLogP) [151–153] as parameters:

log t1/2 = −3.805 + 0.172O= h − 10.146qC= + 0.112QLogP n = 67,

r = 0.899,

␴ = 0.356,

F = 88.1

(5.9)

All the parameters in this equation are calculable from the molecular structure and are statistically relevant (p < 0.01). The present form was obtained after omission of a total of 12 outliers. However, eight of these 12 compounds have very short half-lives that are difficult to determine, and the corresponding experimental errors might be considerable, especially on a logarithmic scale. Since a number of different enzymes are probably involved in the hydrolysis of these compounds, one can hardly expect any general description at this level to give a significantly better overall fit. It has to be mentioned, however, that within some of the series, a number of compounds were found not to be metabolized in any significant amount and the corresponding (large) half-lives were not reported at all. For most of these compounds, our model

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fails to predict a half-life significantly larger than those of their structurally similar analogs. Some of their structural features might hinder their fit into the active site of the metabolizing enzyme(s). QSMR Model: Predictive Power The predictive power of the model based on eq. (5.9) was tested on five compounds with completely unrelated structures: vinyl acetate, isocarbacyclin methyl ester, glycovir, clevidipine, and itrocinonide [33,62]. It is unrealistic to expect accurate predictions of hydrolytic half-lives for arbitrary structures. Nevertheless, the present method should prove useful in distinguishing on the basis of chemical structure alone among compounds whose hydrolysis is fast, medium, or slow. Two important warnings have to be mentioned here. First, considering that eq. (5.9) is based on logarithmic half-lives and has a standard deviation of ␴ = 0.36, considerable errors in the actual half-lives predicted are possible. Second, because h is considerably conformation sensitive, and because a small change in its value can cause large variations in the calculated t1/2 , careful conformational sampling is required to find the less hindered energetically favorable conformation before any estimates are made. Obviously, eq. (5.9) in its present form cannot account for any specific effect. For example, insertion of a heteroatom substituent (in particular, sulfur, SO, or SO2 ) in the ␤ or ␥ position relative to the carbonyl was noted to increase the rate of enzymatic hydrolysis dramatically [154]. Similar observations were also made for ester prodrugs of benzoic acid [155], soft ACE inhibitors [156], or soft ␤-blockers [157]. For such sulfur atoms, a possible role in stabilizing the tetrahedral structure of the hydrate in the enzyme–inhibitor complex has been suggested based on an x-ray crystal structure of a hydrated trifluoromethyl ketone that showed intramolecular hydrogen bonding between the S atom and the − −OH group on the carbonyl of the hydrate [154]. Such trifluoromethyl ketones are putative transition-state esterase inhibitors, thought to act by forming a tetrahedral covalent hydrate with the catalytically active serine of carboxylesterases.

5.1.9

Rate-Influencing Role of the Alcohol or Acyl Side Chain

Because in the QSMR equation for enzymatic hydrolysis [eq. (5.9)], most unexplained variance is intraseries and not interseries, we attempted to compare the rateinfluencing role of the alcohol and acyl side chains using data available for congener series with simple side chains (e.g., Me, Et, Pr, Bu, Pe, Hx, iPr, sBu, tBu). For alcohol side chains, relatively consistent results were obtained [34] on the basis of in vitro human blood (or plasma) hydrolysis data for the following series: soft opioid analgetics [40], soft antiarrhythmics [158], soft nitrogen-containing cannabinoid analogs [159], ibuprofen prodrugs [160], and benzoic acid prodrugs (the only series containing aromatic esters) [155]. Among straight-chain substituents, butyl tends to result in the fastest degradation of aliphatic esters (Figure 5-6). Both shorter and longer alcohol chains result in slower degradation. This might not be true for aromatic esters, but since comparable data from only one series (benzoic acid prodrugs) were available,

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15 12.3 Relave half-life (Bu = 1)

11.1 10

5

3.3

3.0

2.9 1.9 1.0

0 0

1

2

3

4

5

6

7

8

Length of alcohol chain

FIGURE 5-6. Influence of the length of the alcohol side chain on the rate of enzymatic hydrolysis as illustrated by the average relative in vitro human blood or plasma hydrolysis half-lives for various straight-chain alcohol substituents in different congener series of aliphatic esters [34].

it is difficult to make general assumptions. Obviously, increasing branching causes increasing steric hindrance and consequently increasing hydrolytic half-lives. In fact, data for tert-butyl (tBu) substituents are probably missing from the comparison because the corresponding compounds were too slow to hydrolyze to be of interest as possible soft drugs or prodrugs. For simple acyl side chains, much less consistent results were obtained. In vitro human blood or plasma hydrolysis data were included for the following series: etilefrine prodrugs [161], buprenorphine prodrugs [162], pilocarpine prodrugs [163], enol ester prodrugs [164], oxprenolol prodrugs [165], bispilocarpine diester prodrugs [166], and metronidazole prodrugs [167]. Even if the overall picture is much less consistent then for the alcohol side chain, it can be concluded that a length of about three or four carbon atoms is also needed for fast hydrolysis, and the often used acetyl derivatives may not provide the shortest half-lives. Hence, available in vitro human blood data suggest that shortest half-lives are achieved with sterically nonhindered alcohol and acyl chains that are neither too short nor too long and are around four carbon atoms long.

5.2

SOFT DRUG APPROACHES

Following this discussion on the enzymatic aspects, let’s return now to the discussion of the soft drug concept. The SD concept as such was introduced in 1976 [1] and

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reiterated in 1980 [168–170]. Since then, a total of five major SD approaches have been identified [2,3,6–9,171]: 1. Inactive metabolite-based soft drugs: active compounds designed starting from a known (or hypothetical) inactive metabolite of an existing drug by converting this metabolite into an isosteric or isoelectronic analog of the original drug such as to allow a facile, one-step controllable metabolic conversion, after the desired therapeutic role has been achieved, back to the very inactive metabolite from which the design started. 2. Soft analogs: close structural analogs of known active drugs that have a specific metabolically sensitive moiety built into their structure to allow a facile, onestep controllable deactivation and detoxication after the desired therapeutic role has been achieved. 3. Active metabolite-based soft drugs: metabolic products of a drug resulting from oxidative conversions that retain significant activity of the same type as the parent drug. The corresponding basic principle is that if activity and pharmacokinetic considerations allow it, the drug of choice should be the metabolite at the highest oxidation state that still retains activity. 4. Activated soft drugs: a somewhat separate class derived from nontoxic chemical compounds activated by introduction of a specific group that provides pharmacological activity. During expression of activity, the inactive starting molecule is regenerated. 5. Pro-soft drugs: inactive prodrugs (chemical delivery forms) of a soft drug of any of the classes above, including endogenous soft molecules. They are converted enzymatically into the active soft drug, which is subsequently enzymatically deactivated. Among these approaches, the inactive metabolite-based and soft analog approaches have been the most useful and successful strategies for designing safe and selective drugs; they overlap somewhat and are not always clearly distinguishable. Both of these approaches focus on designing compounds that have a moiety that is susceptible to metabolic, preferentially hydrolytic, degradation built into their structure. This allows a one-step controllable decomposition into inactive, nontoxic moieties as soon as possible after the desired role is achieved and avoids other types of metabolic routes. Of course, a judicious combination of de novo (e.g., receptorbased) design principles with general soft drug design principles can also result in de novo soft drugs (see, e.g., the soft cytokine modulators described in Section 5.4.9). Even before the formal introduction of the concept of metabolically labile soft drugs (late 1970s), there were therapeutic agents that made use of the advantages attainable by introduction of an ester moiety into the structure. One of the earliest was etomidate (Amidate) (5-1, Figure 5-7). This is a unique short-acting nonbarbiturate hypnotic agent discovered in 1964 [172]. It is eliminated by ester hydrolysis in plasma and liver [173]. Etomidate is a potent i.v. hypnotic agent with a very rapid onset of action. Its acid metabolite is inactive, and the duration of hypnosis after etomidate

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Cl -

O (R)

O

O

N O

N

N

Cl -

O

O N 5-1

5-2

etomidate

succinylcholine (chloride)

O

O N

N

N H H 2N

O

5-3

H 2N

5-5

procainamide (t1/2 = ~3h)

procaine (t1/2 = ~1min) O

H N

O

N O 5-4 lidocaine

H 2N 5-6 benzocaine

FIGURE 5-7. Etomidate (5-1), a short-acting hypnotic agent, and succinylcholine (5-2), a short-acting depolarizing neuromuscular agent, are early examples of compounds with a shortacting, safe character provided by esterase-mediated hydrolysis. Along similar lines, estercontaining local anesthetics such as procaine (5-5) or benzocaine (5-6) typically have a shorter duration of action than their corresponding amide-containing analogs, such as procainamide (5-3) or lidocaine (5-4), because of their increased susceptibility to hydrolytic degradation.

administration can be very short (< 5 min) [174]. Therefore, the TI of etomidate (18.0 to 32.0) is considerably larger than that of other hypnotic agents, such as thiopental (2.5 to 4.3) and methohexital (4.9 to 11.7) [174]. Unfortunately, etomidate inhibits 11␤-hydroxylase and can cause suppression of adrenocortical steroid synthesis; therefore, its use as a continuous infusion to maintain anesthesia or sedation has been abandoned almost entirely. A soft analog approach to develop an ultrashortacting alternative (MOC-etomidate) to avoid these problems by incorporating an ester moiety in the structure that is more sensitive to hydrolytic degradation than the relatively hindered one of etomidate (1) is discussed later (see Section 5.4.14). Short-acting ester-containing neuromuscular drugs such as succinylcholine (Anectine, 5-2, Figure 5-7) and mivacurium chloride (Mivacron) [175], designed to undergo hydrolysis by human plasma cholinesterase, exploit similar principles to ensure fast and spontaneous recovery upon cessation of administration. Hence, their durations of action are only 6 to 8 and 12 to 18 min, respectively, and the corresponding

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elimination routes remain functional even in renal failure. Local anesthetics provide another example. They can be considered as being composed of three main building blocks: a lipophilic (usually aromatic) group and an ionizable (usually tertiary amine) group connected by an alkyl/alylene chain that incorporates either an amide or an ester linkage (Figure 5-7) [176]. Because of their susceptibility to hydrolytic degradation, ester-type local anesthetics such as procaine (5-5), benzocaine (5-6), and cocaine, typically have a shorter duration of action than that of amide local anesthetics such as lidocaine (5-4), bupivacaine, and prilocaine. For example, procaine (5-5; Novocain, derived originally from “novus” and “caine” from cocaine) has a very short half-life of about 1 min as it is hydrolyzed to para-aminobenzoic acid (PABA, which may cause allergic reactions and inhibit the action of sulfonamides), whereas the analogous procainamide (5-3) has a considerably longer half-life of about 3 h. As amides are much less prone than esters to hydrolysis, the major mode of clearance of procainamide is indeed renal and not metabolic, and very little PABA is observed as a metabolite. For some of the amide-type local anesthetics, soft analogs also exist–they incorporate an additional ester side chain that is metabolized to a corresponding inactive acid; these compounds (e.g., articaine) are discussed later.

5.3

INACTIVE METABOLITE–BASED SOFT DRUGS

The inactive metabolite–based approach is a very versatile method for developing new and safe drugs, and, as of today, the most successful SD design strategy. The design process starts from a known or a designed inactive metabolite of a drug used as a lead compound. Starting from the structure of this inactive metabolite, novel structures are designed that are isosteric and/or isoelectronic analogs of the drug from which the lead inactive metabolite was derived (isosteric/isoelectronic analogy). These new structures are designed in such a way as to yield the starting inactive metabolite in a single metabolic step (metabolic inactivation) without any other metabolic conversions (predictable metabolism). The specific binding and transport properties as well as the metabolic degradation rates of the new SDs can be controlled by structural modifications (controlled metabolism). The entire corresponding design process has been described in detail [10]. How much freedom is available to make structural modifications while designing the new structures depends on how involved the restrictive pharmacophore regions are in formation of the inactive metabolite. Evidently, if they are not involved, there is more freedom for structural modifications, and one can deviate considerably from the requirement of the isosteric/isoelectronic analogy. Inactive metabolite–based SDs can also be obtained by starting not from an actual isolated and identified inactive metabolite, but from a designed useful inactive metabolite (a hypothetical inactive metabolite). Obviously, the inactivity of this metabolite will have to be confirmed in a later stage of the developmental process. Nevertheless, the design of such esterbased SDs is greatly enhanced by the fact that the introduction of a carboxylic acid moiety can often fundamentally change the biological activity, especially in smaller molecules. Very often, the initial biological activity is destroyed and the toxicity of

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97

the parent compound is greatly reduced [177]. Compare, for example, the toxic aniline (LD50 of 0.44 g/kg p.o., rats) with the nontoxic, former antirickettsial PABA (LD50 > 6.0 g/kg p.o., rats), the sympathomimetic phenethylamine (LD50 of 0.47 g/kg s.c., mouse) with the inactive phenylalanine, the hypnotic tert-pentyl alcohol (2-methyl-2-butanol) (LD50 of 1.0 g/kg p.o., rats) with the inactive 2,2dimethylbutyric acid, or even the toxic antiseptic phenol (LD50 of 0.53 g/kg p.o., rats) with the nontoxic anti-inflammatory salicylic acid. Hence, such acids can often serve as a starting point for hypothetical inactive metabolite–based SD design approaches if their esterification can restore the original biological activity. Obviously, this is not a generally valid rule, as in many structures, especially in larger ones, pharmacological activity is maintained despite the presence of carboxylic groups [e.g., ␤-lactam antibiotics, antihistamines (see Figure 5-66), anti-inflammatory arylacetic acids, or prostaglandins]. To illustrate the general inactive metabolite–based SD design principles and their specific applications, a number of drug classes will be reviewed, starting with those that have already resulted in marketed drugs. 5.3.1

Soft Beta-Blockers

Beta-adrenergic blockers provide many examples, because in this class, inactive metabolite–based SDs can be obtained by introducing the hydrolytically sensitive functionality at a flexible pharmacophore region not critical for activity; therefore, there is considerable freedom for structural modifications. Consequently, transport and metabolism properties can be controlled more easily. By blocking the cardiac ␤-adrenergic receptors, ␤-blockers protect the heart from the oxygen-wasting effect of sympathetic inotropism; an effect that is utilized prophylactically in angina pectoris to prevent myocardial stress that could trigger an ischemic attack [178]. They are also used to lower the cardiac rate and to protect the failing heart against excessive sympathetic drive, to lower elevated blood pressure, and to manage elevated intraocular pressure in glaucoma following topical application to the eye. Since the introduction of propranolol (1-11) in the late 1960s, many other agents have become commercially available, including, for example (in more or less chronological order), alprenolol, timolol, metoprolol, nadolol, penbutolol, betaxolol, bopindolol, esmolol, and nebivolol [178]. Design Considerations The metabolism of the well-known ␤-blocker metoprolol (5-7) is compared with that of the corresponding soft drugs (5-12), designed starting from one of its inactive metabolites (5-11) in Figure 5-8. Metoprolol is extensively metabolized by the hepatic monooxygenase system both at the more restrictive ␤-amino alcohol pharmacophore region (pathway A, resulting in 5-8) and at the more flexible pharmacophore region para to the phenol ring (pathways B and C, resulting in 5-9 and 5-10, respectively) [179–181]. Two of these metabolites, ␣-hydroxymetoprolol (5-9) and O-demethylmetoprolol (5-10), have selective ␤1 -blocker activity, but are 5 to 10 times less potent than metoprolol itself [179]. The main metabolites detected are, however, the acids 5-8 and 5-11, and they are inactive in the sense that they are devoid of ␤-adrenoceptor activity or toxicity (LD50 in mice is greater than 500 mg/kg i.v.)

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OH

O

NH

O

C

O

NH

OH

5-7

5-10 B

metoprolol

OH O

O-demethylmetoprolol NH

O A

5-9 OH

OH

O

OH

α-hydroxymetoprolol O

O

NH

OH

O OH

O

5-8

5-11 metoprolol, acid metabolite 2 (inactive)

metoprolol, acid metabolite 1 (inactive)

soft drug design OH

OH R

O

O

NH

O

5-12 n

soft analog (general structure)

NH

OH

O O

5-13 n

inactive metabolite (general structure)

FIGURE 5-8. Comparison of the metabolism of metoprolol (5-7) with that of the corresponding soft drugs (5-12), designed starting from one of metoprolol’s inactive acid metabolites (5-11). The dashed lines in the general structures 5-12 and 5-13 indicate the possibility of having either isopropyl– or tert-butyl–substituted amines.

[179]. Hence, the phenylacetic acid derivative 5-11 can be used as a starting point for an inactive metabolite approach. By esterification of 5-11 and by introduction of some additional flexibility in the design (e.g., n = 0 or 2 and not just n = 1), a number of soft ␤-blocker structures (5-12) can be obtained with different receptor binding, transport, rate of cleavage, and metabolic properties. Soft drugs are ideally suited for ophthalmic applications (Figure 5-1), which is especially important, as most ophthalmic drugs were not designed and developed specifically for the treatment of eye diseases [182,183]. Despite the apparent easy accessibility of the eye for topical treatments, various defense mechanisms make it difficult to achieve an effective, and if possible localized, concentration within the eye. The cornea is the main biological barrier to drug penetration, and it is very effective, as the corneal epithelium has tight annular junctions. In addition, topically applied drugs are rapidly eliminated (washed away by the tear flow) from the precorneal area, which can, in any case, hold only a relatively limited volume. A more detailed review of the challenges of ocular targeting is included in Section 6.2.1, but it is worth remembering that in most cases, only about 2% of the medication introduced to the eye is actually absorbed there. The rest is washed away and absorbed through the nasolacrimal duct and the mucosal membranes of the nasal, oropharyngeal, and gastrointestinal tract, thereby reaching the systemic circulation. Various approaches have been tried to circumvent this problem of low ocular delivery and potential for substantial systemic side effects [183,184]; SDs are particularly well suited. Beta-adrenergic blockers were found to be beneficial in reducing the elevated

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intraocular pressure (IOP) associated with glaucoma, and they are now a major class of drugs used in glaucoma patients (e.g., timolol, betaxolol, carteolol). However, ␤-blockers not only retain their cardiovascular activity when applied topically to the eye and are responsible for significant cardiovascular side effects, but even major respiratory events have been reported in association with the ophthalmic use of various ␤-blockers [185–187]. Adaprolol For therapeutic applications where membrane transport, which tends to be lipophilicity related, and relative stability are important to achieve pharmacological activity, the R group of 5-12 should be selected so as to provide sufficient lipophilicity and ester stability. For example, this could be the case for ocular applications, as lipophilicity and some metabolic stability are needed for good corneal permeability. Accordingly, from the various soft ␤-blockers developed in our laboratory at the Center for Drug Discovery, University of Florida (n = 1), adaprolol, the adamantane ethyl derivative (5-15; Figure 5-9), was selected as a candidate for a topical antiglaucoma agent [188–192]. Adaprolol produces prolonged and significant reduction of IOP, but it hydrolyzes relatively rapidly [189,190]. Therefore, local activity can be separated

OH

OH O

O

NH

NH

NH2

O O

5-7 metoprolol

5-14 atenolol

OH

OH O

O

NH

O

O

O

NH

O 5-16 esmolol

5-15 adaprolol OH

O O

(S)

O

O

O

(S)

O NH

NH

N

O

5-17 landiolol OH O

O OH

NH O

O NH NH

O O O

5-18 vasomolol

NH2

F 5-19 flestolol

FIGURE 5-9. Representative ester-containing ␤-blocker structures designed as soft drugs (5-15 to 5-19) compared to metoprolol (5-7) and atenolol (5-14) as leads.

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1.0

IOP change (mmHg)

0.5 0.0 -0.5 -1.0 -1.5 ** -2.0 *

**

-2.5 ** -3.0 -3.5 0

1

2

3

Time (h) Treated eye

Control eye

4

5

6

* p < 0.05, ** p < 0.005

FIGURE 5-10. Effect of adaprolol (5-15) on the IOP of rabbits following unilateral treatment (0.1 mL of 0.25% solution administered at 0 and 2 h). Due to the soft nature of 5-15, no effects are produced in the untreated eye. Data are mean ± SEM for four to six animals [190].

from undesired systemic cardiovascular or pulmonary activity, a characteristic much sought after in the search for antiglaucoma therapy [193]. Following unilateral ocular treatment with adaprolol in rabbits, no effects are produced in the contralateral eye because of systemic inactivation of this soft drug (Figure 5-10). A similar experiment has been performed using separate S(+) and R(–) isomers as well as their racemic mixture, and they indicated that the ocular hypotensive effects are not stereoselective (whereas the cardiac effects might be stereoselective) [192]. Several clinical studies of adaprolol maleate have been completed, and no severe or clinically significant medical events have been reported. A double-masked comparison of adaprolol and timolol performed on ocular-hypertensive patients (IOP > 21 mmHg, n = 67) demonstrated that intraocular pressure was significantly reduced throughout the study in all treatment groups. Adaprolol reduced IOP by about 20%, while timolol reduced IOP by 25 to 30% [9,182]. In patients over 70 years old, the IOP-reducing effects of 0.2% adaprolol and 0.5% timolol were statistically indistinguishable after 10 days of application (Figure 5-11) [9,182]. On the other hand, timolol reduced the systolic blood pressure with statistical significance, while neither of the adaprolol concentrations tested demonstrated such change (Figure 5-12). Timolol also showed a trend, although not statistically significant, to reduce the heart rate, while pulse was conserved in both adaprolol treatment groups. Hence, adaprolol has a safer cardiovascular profile than timolol, especially in the population over 70 years old.

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Time (day)

IOP 0 0

2

4

6

8

10

12

14

IOP change (mmHg)

-2

-4

-6

-8

-10

-12

Change in systolic blood pressure (mmHg)

Adaprolol 0.2%, n = 7

Timolol 0.5%, n = 7

BP

8 6 4 2 0 -2

*

-4 -6

*

*

-8

*

*

-10

*

-12 0

2

4

6

8

10

12

14

Time (day) Adaprolol 0.2%, n = 22

Adaprolol 0.4%, n = 19

Timolol 0.5%, n = 22

* p < 0.05

FIGURE 5-11. Comparison of the changes caused by administration of adaprolol (0.2%) and timolol (0.5%) in the intraocular pressure (IOP, left) and systolic blood pressure (BP, right) of ocular hypertensive patients. IOP data shown are for patients over 70 years old.

Ultrashort-Acting ␤-Blockers For therapeutic applications where systemic ultrashort action is the objective, R groups that make 5-12 susceptible toward rapid hydrolysis should be used. With agents that have short half-lives (ca. 15 min), steady-state plasma concentrations and readily adjustable effects can be achieved rapidly on i.v. administration and infusion. Also, drug effects can be rapidly eliminated by termination of the infusion. To achieve ultrashort action, ester functionalities that are very quickly hydrolyzed have to be built into the structure. A set of possibilities came from our earlier work aimed at designing novel activated-ester prodrugs of

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5-20 aspirin

O O

O O

O O

S

S

O

O O 5-21

5-22

O

5-23 O

O

O O

O

S

S

O

O

5-24

5-25

S O

OH

OH

O

O

O

O

S O

O

O

OH 5-26

O OH OH

5-27 salicylic acid gentisic acid and trihydroxy derivatives (traces) salicylic glucuronide (5%)

salicyluric acid (80%) salicylic phenolic glucuronide (10%)

FIGURE 5-12. Possible metabolic routes of methylthiomethyl (5-21), methylsulfinylmethyl (5-22), and methylsulfonylmethyl 2-acetoxybenzoates (5-23) [194,197,198] designed as possible aspirin (5-20, also 1-2) prodrugs [194–196].

aspirin (acetylsalicylic acid, 5-20, Figure 5-12; also 1-2) [194–196]. Aspirin prodrugs are of interest as a possible way to avoid the well-known gastrointestinal side effects of aspirin, which are to a considerable extent related to its free carboxylic group. Prodrugs that are transiently masking this free carboxyl group could avoid these; however, most attempts were unsuccessful because the o-acetyloxyl group of aspirin is rather labile, allowing its facile conversion into salicylic acid (5-27). Following oral administration, aspirin undergoes significant first-pass hydrolysis in the intestinal wall and liver (approximately 30% in humans), followed by extensive hydrolysis in the blood so that no intact drug is detectable in plasma 2 h after administration of a standard dose [17]. Although 5-27 is also a potent anti-inflammatory agent, 5-20 is a far more potent analgesic, so its delivery to the bloodstream in the intact form is

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preferable. This requires the carboxyl-protecting pro-function to be cleaved prior to aspirin’s own labile o-acetyl, which is difficult to achieve while also maintaining the prodrug stable enough to have a sufficient shelf life. In our work [194–196], we found the methylthiomethyl (5-21), methylsulfinylmethyl (5-22), and methylsulfonylmethyl esters (5-23) of aspirin to be cleaved in vitro in plasma to form aspirin (5-20) rather than the corresponding salicylates [194,197,198]. In vivo studies using dogs indicated that at least one aspirin derivative, methylsulfinylmethyl-2-acetoxybenzoate (5-22), is a true aspirin prodrug since aspirin was detected in the blood after administration of the prodrug. Accordingly, we have also explored a number of similar esters to obtain rapidly hydrolyzing soft ␤-blockers, and indeed a number of methyl–thiomethyl and related esters (5-12; n = 1, R = CH2 SCH3 , CH2 SOCH3 , CH2 SO2 CH3 ) were found to be ultrashort acting [157]. When injected intravenously, these compounds hydrolyzed much faster than did simple alkyl esters. Various other soft ␤-blockers within this family of general structures (5-12; n = 0 to 3), have been developed and tested in different laboratories. A number of these designs have proven quite successful; they are reviewed briefly below. Esmolol Esmolol (Brevibloc, 5-16) is an ultrashort-acting ␤-blocker also designed to rely on rapid metabolism by serum esterases in a work initiated by Erhardt and coworkers at American Critical Care [199,200]. By the late 1970s, it was already known that insertion of an ester moiety between the aromatic ring and the ␤-amino alcohol side chain [201] or at the more remote para position [202] might not affect ␤-blocking activity significantly. It was also shown that the acid metabolites 5-13 (n = 0, 1) are devoid of ␤-adrenoceptor activity [179,202]. Somewhat later, following a systematic search of different ␤-blocker series that contained ester moieties inserted at different positions, esmolol, a methyl ester (as also reflected by its name ester-methyl), was selected as the best candidate for development [199,203–205]. The duration of action for the compounds of the general structure 5-12 in these series decreased as n increased from 0 to 2, probably because of increasing degradation by hydrolysis with decreasing steric hindrance. Esmolol (n = 2, R = CH3 ; 5-16, Figure 5-9) was the fourth ␤-blocker approved by the FDA (1986) for i.v. clinical use, but it was different from the previous three (propranolol, metoprolol, labetalol) because its pharmacological effects dissipate within 15 to 20 min after stopping administration of the drug [206]. The elimination half-life of esmolol (5 to 15 min) is indeed considerably shorter than that of propranolol (2 to 4 h) [206]. Its acid metabolite formed by hydrolysis of the ester group (5-13, n = 2) has a relatively long half-life (3.7 h), but it is indeed inactive. It has a ␤-adrenoceptor antagonist potency about 1500-fold lower than esmolol, and it is unlikely to exert any clinically significant effects during esmolol administration [206]. The in vitro human blood half-life of esmolol is around 25 to 27 min [38,39]. As a nice confirmation of the SD design principles, the presence of other ester-containing drugs, such as acetylcholine, succinylcholine, procaine, or chloroprocaine, have been shown to have no effect on this hydrolytic half-life, and consequently, no metabolic interactions are to be expected [38].

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Landiolol Landiolol (ONO-1101, Onoact, 5-17) is an ultrashort-acting ␤-blocker with improved cardioselectivity developed more recently at Ono Pharmaceuticals in Japan [207–210]. Modification of the R ester group of esmolol did not afford compounds superior to esmolol in ␤-blocking activity, duration of action, or cardioselectivity. However, additional modifications resulted in landiolol, which, compared to esmolol, has a ninefold increased in vivo ␤-blocking activity and an eightfold increased in vitro ␤1 /␤2 cardioselectivity (255 vs. 32) [207]. Landiolol (5-17) contains a morpholinocarbonylamino moiety and has S-configured hydroxyl and ester functions. It has proved to be a potent ␤-antagonist, with effects that are quickly removed by washout [208–210]. Landiolol was found to have an elimination half-life shorter than that of any other ␤-blocker (2.3 to 4.0 min, in vivo human) [210]. It is used widely in Japan, and clinical use has confirmed that it is valuable as a bridge toward starting oral ␤-adrenergic receptor blockers and as an antiarrhythmic agent. Because of its increased ␤1 -selectivity and its rapid onset and offset of action, it is also suitable for intensive care unit patients [211].

Vasomolol, Flestolol, and Other Structures Vasomolol (5-18) was developed by Chen and co-workers at Kaohsiung Medical College, Taiwan, along quite similar design principles [212]. It is a vanilloid-type ultrashort-acting ␤1 -adrenoceptor antagonist that has vasorelaxant activity and is devoid of intrinsic sympathomimetic activity [212]. As with similar agents, vasomolol infusion was characterized by a rapid onset of action. Steady state of ␤-blockade was attained within 10 min after initial infusion, and a rapid recovery from blockade occurred after discontinuation of the infusion [212]. Because relatively early in the study of ␤-blockers, it was found that insertion of an ester moiety between the aromatic ring and the ␤-amino alcohol side chain preserves reasonable ␤-blocking activity [201,205], a variety of such structures have also been synthesized and investigated. Half-lives in blood and liver suggested that ester hydrolysis is the major pathway for the inactivation of these [(arylcarbonyl)oxy]propanolamines. A bulky 2-CH3 substituent prevented hydrolysis of the ester, but the 2-F substituent, which offered minimum steric hindrance but maximum electron-withdrawing effect, was a promising aromatic substituent for short action [205]. Flestolol (5-19; ACC-9089), a compound with such a 2-F substituent, was selected for further toxicological evaluation and clinical study. All clinical findings were consistent with the SD hypothesis: Flestolol was safe and effective, had a short elimination half-life of about 7 min and thus rapid recovery from ␤-block after termination of infusion, and was cleared mainly by extrahepatic routes [213, 214]. Interestingly, an investigation of the metabolism of flestolol and other esters found polymorphic rates of ester hydrolysis in New Zealand white rabbit blood and cornea [44]. About 30% of the animals studied were found to be “slow” metabolizing (t1/2 = 17 min, in vitro, blood) and about 70% were found to be “fast” metabolizing (t1/2 < 1 min). No such bimodal distribution of esterase activity was found in blood from rats, dogs, and humans or in the aqueous humor and iris–ciliary body complex of rabbits [44].

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Another ␤-antagonist, which has a structure similar to the general structure 5-12 (n = 2) but contains a reversed ester in its para-positioned side chain (L-653,328) [215], was also claimed to be a soft drug [193]. The ocular instillation of 2% of this drug to human volunteers resulted in a reduction in IOP; however, this was less than that elicited by 0.5% timolol. Nevertheless, in contrast to timolol, there was no evidence of systemic ␤-adrenoceptor blockade [216]. In addition, cumulative concentrations of L-653,328 up to 4% did not cause bronchoconstriction in asthmatic patients [217]. Despite these, L-653,328 cannot be considered a true soft drug because its hydrolytic cleavage releases an active alcohol, L-652,698. In fact, L-653,328 was originally designed as the acetate ester prodrug of this active alcohol, and both the ester L-653,328 and the alcohol L-652,698 have modest ␤-receptor blocking activity. The Ki value for displacement of [125 I]iodocyanopindolol binding to ␤1 -binding sites in membrane fractions of rabbit left ventricle is somewhat smaller for the ester: 3.1 vs. 5.7 ␮M, and the more lipophilic ester causes somewhat better IOP lowering [215]. As it turns out, this case represents neither an ideal prodrug nor an ideal SD design. The lack of systemic effects is attributed to the rapid oxidation of the alcohol in the systemic circulation into inactive carboxylic acid metabolites [216]. Soft Bufuralol Analogs Bufuralol (5-28, Figure 5-13) is a potent, nonselective ␤-adrenoceptor antagonist with ␤2 partial agonist properties. Its effectiveness in the treatment of essential hypertension is probably due to a favorable balance of ␤-blockade and ␤2 agonist–mediated vasodilation. Bufuralol undergoes complex OH O

NH

5-28 bufuralol

oxidative metabolites HO

O

O

OH O

OH

OH NH

O

5-29

OH NH

O

NH

5-30

5-31

active metabolite

a b c d

O R = Me R = Et R = iPr R = tBu

soft drug design

R O

OH

O

OH NH

5-32 soft analog

O OH

O

NH

5-33 (hypothetical) inactive metabolite

FIGURE 5-13. The metabolism of bufuralol (5-28) produces oxidative active metabolites (5-29, 5-30) that lead to a final inactive acid metabolite (5-31). Starting from a designed (hypothetical) inactive metabolite (5-33), a series of inactive metabolite-based soft compounds (5-32) were designed.

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metabolism in humans, including stepwise oxidation toward an acid metabolite (5-31) through the corresponding hydroxy (5-29) and keto (5-30) intermediates (Figure 5-13). These intermediates are still active [218,219] and have different (interestingly, longer) elimination half-lives [220]. Not only does a differential metabolism of the two enantiomers occur, but differences due to genetic polymorphism are also encountered [221]. An SD approach may help avoid these problems, and we synthesized a number of ester-containing soft drug candidates (5-32) and then tested them for ␤-antagonist activity by recording electrocardiogram and intraarterial blood pressure in rats [222]. This is an example of a hypothetical inactive metabolite-based approach, as the retrometabolic design starts not from an actual, major metabolite but from a hypothetical one (5-33). Nevertheless, this metabolite was confirmed to be inactive during the study: It did not decrease the heart rate significantly. In the isoproterenol-induced tachycardia model, bufuralol (1 mg/kg i.v.) diminished the heart rate for at least 2 h, whereas the effects of equimolar SDs lasted for only 10 to 30 min (Figure 5-14). The effects of the four most active compounds (5-32, R = methyl, ethyl, isopropyl, tert-butyl) on resting heart rate and mean arterial pressure were also evaluated in comparison to esmolol following infusion (Figure 5-14). These new SDs produced effects that were similar to that produced by esmolol both in magnitude and time course, and the corresponding infusion rates were 10-fold smaller. 5.3.2

Soft Opioid Analgetics: Remifentanil

Several short-acting 4-anilidopiperidine opioids, such as fentanyl (5-34a; also 1-12), sufentanil (5-34b), and alfentanil (Figure 5-15), have been introduced into anesthetic practice because they showed advantages over morphine. They do not cause significant histamine release and have shorter durations of action than those of morphine. Therefore, they can provide greater cardiovascular stability and less persistent postoperative respiratory depression. However, their terminal half-lives in humans are still longer than desired; for example, even the shortest-acting alfentanil has t1/2 ≈ 70 to 90 min. This can result in drug accumulation and prolonged durations of action after multiple bolus injections or infusion. In addition, hepatic dysfunction may result in prolonged retention because the elimination of these compounds relies on hepatic metabolism [223]. A hypothetical inactive metabolite-based SD approach initiated and pursued at Glaxo (now GlaxoSmithKline) proved useful in solving these problems. A first attempt to incorporate ester and carbonate moieties into structure 5-34 (Figure 5-15) at −CH2 O2 CR , − −CH2 O2 COR ) produced potent analgetics, the R2 side chain (R2 = − but durations of action were still longer than desired [224]. However, another design based on esterification of the hypothetical inactive metabolite 5-36 yielded remifentanil (5-35a, Ultiva), a unique ultrashort-acting opioid analgesic [40,225–227]. Even if there is no evidence for the opioid analgetics represented by 5-34 (Figure 5-15) to metabolize into the acids 5-36 [223], structures of type 5-35 can represent possible soft, short-acting compounds susceptible to hydrolytic inactivation. During the synthesis and pharmacological evaluation of a number of such

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Effect on isoproterenol-induced tachycardia 0 % Change in heart rate

-5 -10

5-28, bufuralol

-15

vehicle

-20

5-33, metabolite

-25

5-32a, R=Me

-30

5-32b, R=Et 5-32c, R=iPr

-35

5-32d, R=tBu

-40 -45 0

10

20

30

40

50

60

Time (min)

Effect on resng heart rate 0 % Change in heart rate

-5 -10

vehicle

-15

5-32a, R=Me

-20

5-32b, R=Et

-25

5-32c, R=iPr 5-32d, R=tBu

-30

5-16, esmolol

-35 -40

Infusion

-45 0

10

20

30 Time (min)

40

50

60

FIGURE 5-14. Effects of various bufuralol-related soft drugs (5-32a–d) on isoproterenolinduced tachycardia (i.v. bolus of 3.8 ␮mol/kg; vehicle 10% DMSO in 30% hydroxypropyl ␤-cyclodextrin) and resting heart rate (i.v. infusion, 2 ␮mol/kg·min, R = Et: 4 ␮mol/kg·min, esmolol: 20 ␮mol/kg·min; vehicle 0.9% NaCl) in rats. Data represent the mean of at least three animals per group; error bars omitted for better visibility.

compounds [40], it was established that the carfentanil (5-34c) piperidine (R2 = CO2 CH3 ) provided more potent analgetics than the fentanyl nucleus (R2 = H). A separation of two methylene units (n = 2) between the piperidine nitrogen and the ester function was found as optimal for added potency and decreased duration of action [40]. Durations of actions, as measured in vivo by a classic rat tail withdrawal assay [228], ranged from extremely short to long (5 to 85 min), depending on the substitution of the alkyl portion R of the ester. One of these compounds, remifentanil (5-35a, Figure 5-15), was approved by the FDA in 1996 for clinical use as an ultrashort-acting opioid analgetic (Ultiva) during general anesthesia and monitored

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O N

N

R2

H

N

O

O

5-34b sufentanil

soft drug design N

5-35

N

R2

n

O

R

O N

OH 5-36 n (hypothetical) O inactive metabolite

O

O N

N O

5-34c carfentanil

O

O

O N

N S

O N

R2

O

O

O N

5-34a fentanyl

n R1

O

O N

N

5-34

N

5-35a

O

N O

5-36a

O OH

remifentanil

FIGURE 5-15. Design of soft opioid analgetics (5-35) based on the hypothetical inactive metabolite (5-36) as illustrated by general structures (left). The structure of the final, clinically approved SD remifentanil (5-35a) compared to that of other opioid analgetics, such as fentanyl (5-34a, also 1-12) or carfentanil (5-34c), is shown on the right.

anesthesia care. Remifentanil has a half-life of 37 min in human whole blood (in vitro) and is nearly quantitatively converted to the corresponding acid (5-36a). Furthermore, the carboxylic acid (5-36a) was indeed found to be approximately 1000 times less potent in the in vitro guinea pig ileum assay and 350 times less potent in the in vivo rat tail withdrawal reflex model than its parent drug 5-35a. Remifentanil is roughly equivalent in potency to fentanyl in the guinea pig ileum assay (EC50 ’s of 2.4 and 1.8 nM, respectively), and its effect can be antagonized by naloxone, an opiate antagonist [225]. In 24 patients undergoing elective inpatient surgery, its terminal half-life ranged from 10 to 21 min, while that of its major metabolite (5-36a) ranged from 88 to 137 min [227]. Overall, PK studies showed that remifentanil has a rapid onset of action, a small volume of distribution, rapid redistribution, and clearance with a terminal elimination half-life of 8 to 40 min considerably less than alfentanil (60 to 120 min) or fentanyl (180 to 300 min) [229]. The rapid offset in remifentanil concentration is best demonstrated by its context-sensitive half-time, which is extremely short: 3 to 5 min regardless of the duration of infusion [229]. This PK parameter is derived from computer simulations that determine the time needed for the plasma drug concentration to decrease by half (50%) after an infusion scheme designed to obtain and maintain a steady concentration for a given duration. Accordingly, the context-sensitive t1/2 includes both the contribution of elimination and redistribution to the rate of concentration decline. Because the contribution of redistribution to the rate of decline is dependent on the duration of the infusion, the time for a 50% decline is context-sensitive (i.e., it is dependent on the duration of the infusion). When remifentanil is administered within the appropriate clinical range,

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this very short context-sensitive half-time, combined with a rapid rate of equilibration between plasma and its effect compartment, result in a very rapid offset of opioid effect once administration is terminated. After a 3-h infusion of remifentanil or equipotent concentrations of alfentanil, full recovery of respiratory drive occurred within 15 min for remifentanil, but only after more than 45 min for alfentanil. Also, patients administered remifentanil over an 80-fold range of infusion rates were all breathing adequately within 10 min after the infusions ([229] and references therein). In the case of remifentanil, it was proved again that the possibility of drug interactions could be minimized by building metabolic considerations into the structure, as predicted by the basic principles of SD design. Clearance, volume of distribution, and terminal half-life data indicated that coadministration of esmolol had no significant (p < 0.05) effect on the pharmacokinetics (or pharmacodynamics) of remifentanil in rats despite both drugs being metabolized by nonspecific esterases [230,231]. Notably, there is also clinical evidence that because of its hydrolytic metabolism, the PK of remifentanil is independent of end-organ failure, as its pharmacokinetics were unaltered in patients with documented hepatic or renal failure [229]. Also, whereas remifentanil readily crosses the placenta like other opioids of its class (piperidine), unlike the other opioids it is rapidly metabolized in the fetus [229]. More than 10 years after its approval, a detailed quantitative analysis comparing remifentanil with other short-acting opioids (i.e., fentanyl, alfentanil, sufentanil) used for general anesthesia based on results from 85 trials that included a total of 13,057 patients [232], found that consistent with its design as a soft drug, postoperatively, remifentanil was associated with faster recovery and fewer respiratory events requiring naloxone (but also with more frequent postoperative analgesic requirements). Intraoperatively, remifentanil was associated with clinical signs of deeper analgesia and anesthesia (e.g., fewer responses to noxious stimuli, more frequent episodes of bradycardia, more hypotension, and less hypertension) [232]. Remifentanil is a good illustration of a soft drug approach, as it resulted in a drug that has pharmacodynamic properties similar to those of other potent ␮-opioid receptor agonists while it also possesses unique advantages due to its very rapid onset and offset of effect, regardless of the duration of its administration. This results in an opioid that is easy to titrate and can provide profound intraoperative analgesia either for very brief periods if that is desired for the procedure or over prolonged periods without having to be concerned about a prolonged recovery. 5.3.3

Soft Corticosteroids

Anti-inflammatory corticosteroids represent one of the most active fields for SD design; therefore, it is worth reviewing this field briefly to provide adequate background information. Corticosteroids exert profound biological effects in almost every organ; hence, they are one of the most widely used drug classes [233–235]. They are commonly utilized in a variety of clinical diseases and are even the drug of choice and the mainstay of therapy in many of them, due primarily to their potent anti-inflammatory and immunosuppressive effects [233,236,237]. Despite the long and widespread use of glucocorticoids and the considerable time since the concept of

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steroid hormone receptor action was laid down [238], the molecular mechanism of their action(s) started to emerge only in the last 20 years [239–241]. These steroids exert their main effect by binding to glucocorticoid receptors (GRs), a member of the steroid–thyroid–retinoid receptor superfamily [242,243]. Not surprisingly, GR, together with the histamine H1 receptor, are the targets with the most number of drugs approved [244]. GRs are predominantly localized to the cytoplasm of target cells and move into the nuclear compartment only on binding of the glucocorticoid. The crystal structure of the human GR ligand-binding domain bound to dexamethasone (5-42), mifepristone (RU 486; 5-50), as well as deacylcortivazol, has been determined by using receptors with single-point mutations (e.g., F602 S, C638 D) [245–247]. It should be mentioned that even if steroids exert their main action via binding to these hormone receptors that regulate the expression of corticosteroid-responsive genes, there is increasing evidence that corticosteroids can exert nongenomic effects as well [248–250]. There also seems to be a physical and functional interaction between the glucocorticoid receptor and the T-cell receptor complex underlying the nongenomic glucocorticoid-induced immunosuppression in T-cells [251]. A number of structural requirements for glucocorticoid and mineralocorticoid activities are now commonly accepted [233]. Some of the more important ones are summarized in Figure 5-16 together with the common numbering and notation system of these structures; more detailed reviews have been published [234,235]. Table 5-1 summarizes the structure and the GR binding data of a large number of the corticosteroids discussed herein. As mentioned, the therapeutic potential of a compound is a function of various properties determining both the efficacy of the stimulus–response mechanism [252] and the overall absorption, distribution, metabolism, and excretion (ADME) behavior, with receptor-binding affinity (RBA) being, in general, a major determinant. For corticosteroids, it is even more so, because GRs from different tissues and even from different species seem to be essentially the same; consequently, relative RBAs [rRBAs; usually expressed as percent values with dexamethasone (5-42) as reference] are commonly used as a measure of potency. Indeed, for corticosteroids, various in vitro and in vivo pharmacological properties tend to correlate closely with RBA [253]; for example, RBA has been shown to be related to the clinical efficacy of inhaled glucocorticoids [254], to side effects such as cortisol suppression [255, 256], or to immunosuppressive potency [257]. Accordingly, for example, for inhaled corticosteroids [258], average recommended daily doses are closely correlated with rRBA (Figure 5-17A), the only exception being beclomethasone dipropionate (BDP, 5-48), which, however, is a prodrug and has to be transformed into the active 17␣-monopropionate (BMP) form. As customary in quantitative structure–activity relationship (QSAR) studies, the logarithm of the inverse of the dose (log 1/D) is represented as a function of the log rRBA values. Unfortunately, because of the intrinsic multiple activities of steroids and because of the ubiquitous distribution of the corticosteroid receptors, unwanted side effects tend to closely parallel therapeutic effectiveness. Systemic side effects, which typically include myopathy, osteoporosis, hypertension, insulin resistance, weight gain, fat redistribution, increased intraocular pressure, growth inhibition, and others in a dosedependent manner [259–264], still seriously limit the application of glucocorticoids.

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FIGURE 5-16. General corticosteroid structure (5-37) with commonly accepted structure– activity relationship assumptions (top) and selected representative structures discussed in the text.

With the introduction of newer inhaled or intranasal corticosteroids, at least the therapeutic index of this class increased significantly: whereas local activity and clinical efficacy were preserved or enhanced, most of the serious and immediately apparent side effects were virtually eliminated [265]. For most of these compounds, the risk of serious adverse events (SAEs) is also relatively low; for example, for inhaled budesonide (5-41), the incidence of reported SAEs is 1 event per 100,000 patient years [266]. Nevertheless, even inhaled corticosteroids are often underused

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TABLE 5-1. Structure and Glucocorticoid Receptor Binding Data of Representative Corticosteroids Discussed in This Book X6

X9

– + +

H H H

H Cl Cl

H ␤-CH3 ␤-CH3

R␤

log Pcalc

Ve (˚A3 )

Fl/Ac

rRBA


G0 (kJ/mol)

No OH H COCH2 CH3

CH2 OH, 18-aldehyde CH2 OH CH2 OH

1.37 2.36 3.15

283.75 306.37 349.59

0 1 1

10 76 1440

−40.99 −46.02 −53.31

65.86 8.67 0.46

X16

R␣

K D,est (nM)

␤-CH3

COCH2 CH3

CH2 OCOCH2 CH3

4.37

392.73

1

140

−47.53

4.70

+ + + + + + + + + + + + + – +

F H H H H H H H F F H H F H F

F H H H H F F H H F F F F H F

␣-O␣-O␣-O␣-O␣-O␤-CH3 ␣-CH3 H ␣-O␣-CH3 ␣-COOCH3 ␣-COOCH3 ␣-OH ␣-O-

CH(CH2 )2 CH3 , R acetonide CH(CH2 )2 CH3 , R acetonide CH(CH2 )2 CH3 , S acetonide CHcHex, R acetonide CHcHex, R acetonide COCH2 CH3 H COCHCl2 C(CH3 )2 , acetonide COCH2 CH3 H H C(CH3 )2 , acetonide H CH(CH2 )2 CH3 , acetonide

2.75 2.85 2.85 5.14 3.87 3.97 2.03 4.44 2.25 3.76 1.34 2.01 2.74 1.67 3.92

343.50 336.81 336.83 428.05 370.56 349.86 298.01 347.26 326.05 357.82 320.01 349.84 394.77 286.00 415.87

1 1 1 1 1 1 1 0 1 1 1 1 1 0 1

1080 1120 420 15 1681 6300 100 200 165 1796 11 4 200 10 800

−52.60 −52.69 −50.25 −42.00 −53.69 −56.97 −46.70 −48.42 −47.94 −53.86 −41.11 −38.72 −48.42 −40.90 −51.85

0.61 0.59 1.57 43.76 0.39 0.10 6.59 3.29 3.99 0.37 62.73 164.66 3.29 68.13 0.82

LE5601 LE5602 LE5603 LE5610 LE5628 LE5629

– + – + + +

H H H H H H

H H H H F F

H H H H ␣-CH3 ␣-CH3

COOCH2 CH3 COO(CH2 )3 CH3 COOCH(CH3 )2 COOCH(CH3 )2 COOCH2 CH3 COOCH(CH3 )2

CH2 OH CH2 OH CH2 OH CH2 OCOCH(CH3 )2 CH2 OH CH2 Cl CH2 OH OCH2 CH3 CH2 OH SCH2 F CH2 OH CH2 OCOCH3 CH2 S-␥ -lactone ring CH2 OH CH2 OCH(CH3 ) OCOOCH2 CH3 OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl

3.34 4.25 3.69 3.63 3.69 4.04

348.74 370.54 362.67 356.54 359.72 373.89

0 0 0 0 1 1

150 110 70 200 740 560

−47.70 −46.93 −45.81 −48.42 −51.66 −50.97

4.39 5.99 9.41 3.29 0.89 1.18

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Cl

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Aldosterone Beclomethasone Beclomethasone 17-monopropionate Beclomethasone dipropionate Budesonide 22R, 6␣,9␣-F Budesonide, 22R Budesonide, 22S Ciclesonide Ciclesonide, act. metab. Clobetasol propionate Dexamethasone Etiprednol dicloacetate (ED) Flunisolide Fluticasone propionate FP16CM FP16CM 21-acetate Glaxo, ␥ -lactone Hydrocortisone (cortisol) Itrocinonide

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1,2

OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 F OCH2 Cl OCH2 Cl OCH2 Cl OCH2 F OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 F OCH2 F OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl OCH2 Cl CH2 Cl CH2 Cl CH2 OH CH3 , no 11-OH (No CO) SCH3 CH2 OH CH2 OH

4.04 3.69 4.04 3.83 4.18 5.16 3.66 3.64 3.99 4.13 3.80 4.13 4.00 3.07 3.15 3.09 4.07 3.18 3.25 2.93 3.86 3.43 3.92 4.40 3.48 3.31 3.11 4.53 1.60 3.92 4.22 0.78 2.30

374.08 360.01 373.20 362.60 373.87 401.94 365.85 363.09 377.12 377.34 365.97 373.81 373.97 328.52 345.83 345.98 373.96 334.45 348.46 306.65 334.69 326.72 340.66 354.73 343.12 345.88 314.08 367.93 280.45 268.89 315.91 291.24 326.06

1 1 0 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1

440 820 80 210 870 840 820 2100 1100 1000 990 1000 820 180 1200 990 1460 200 70 10 315 29 132 124 54 150 88 1833 19 16 1390 45 270

−50.37 −51.91 −46.14 −48.54 −52.06 −51.97 −51.91 −54.24 −52.64 −52.41 −52.38 −52.41 −51.91 −48.15 −52.86 −52.38 −53.34 −48.42 −45.81 −40.99 −49.54 −43.63 −47.39 −47.23 −45.17 −47.70 −46.38 −53.91 −42.58 −42.17 −53.22 −44.69 −49.16

1.50 0.80 8.23 3.14 0.76 0.78 0.80 0.31 0.60 0.66 0.67 0.66 0.80 3.66 0.55 0.67 0.45 3.29 9.41 65.86 2.09 22.71 4.99 5.31 12.20 4.39 7.48 0.36 34.66 40.82 0.47 14.80 2.44

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COOCH(CH3 )2 COOCH2 CH3 COOC6 H5 COO(CH2 )2 CH3 COO(CH2 )2 CH3 COO(CH2 )4 CH3 COOCH(CH3 )2 COOCH2 CH3 COOCH(CH3 )2 COO(CH2 )2 CH3 COO(CH2 )2 CH3 COO(CH2 )2 CH3 COOCH(CH3 )2 COOCH3 COOCH3 COOCH3 COO(CH2 )2 CH3 COOCH2 CH3 COOCH(CH3 )2 CH3 (CH2 )2 CH3 CH2 CH3 (CH2 )2 CH3 (CH2 )3 CH3 CH2 SCH3 COOCH2 CH3 H CO(2-furoate) H No OH (No O) SCH2 CH3 H C(CH3 )2 , acetonide

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␤-CH3 ␤-CH3 H H ␣-CH3 ␣-CH3 ␣-CH3 ␣-CH3 ␣-CH3 ␣-CH3 ␣-CH3 ␣-CH3 ␣-CH3 H ␣-CH3 ␤-CH3 ␤-CH3 H H H H H H H H H ␣-CH3 ␣-CH3 H H H ␣-OH ␣-O-

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H H H H H H H F F F H F F H F H H H H H H H H H H H H H H H H H H

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Source: Data from [279].

+ + + – + + + + + + + + + + + + + + + + + – – – – + + + + – + + +

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LE5638 LE5639 LE5643 LE5644 LE5648 LE5658 LE5671 LE5673 LE5689 LE5690 LE5693 LE5698 LE5699 LE5702 LE5704 LE5707 LE5709 LE5711 LE5712 LE5725 LE5726 LEGH02 LEGH03 LEGH04 LEGH05 Loteprednol etabonate (LE) Mometasone Mometasone furoate Prednisolone Progesterone Tipredane Triamcinolone Triamcinolone acetonide

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log 1/Dose (average daily low or medium dose, µg/day)

-2.0

Average daily low dose, Dlow Average daily medium dose, Dmedium

-2.2

FP

-2.4

log 1/D low = 0.645 log rRBA – 4.352 r2 = 0.978

BMP (formed from BDP)

BUD

-2.6 -2.8 -3.0

FLU

TAA log 1/D medium = 0.579 log rRBA – 4.491 r2 = 0.887

-3.2 -3.4 2.0

2.2

2.4

A

2.8

3.0

3.2

3.4

log rRBA

-2.0

log 1/Dose (cor
sol suppression, µg/day)

2.6

CS10 daily dose CS20 daily dose

-2.2

FP

-2.4

log 1/CS 10 = 0.924 log rRBA – 5.078 r2 = 0.985

BUD

-2.6

BMP (formed from BDP)

-2.8 -3

TAA FLU

-3.2

log 1/CS 20 = 0.926 log rRBA – 5.408 r2 = 0.985

-3.4 -3.6 2.0

B

2.2

2.4

2.6 2.8 log rRBA

3.0

3.2

3.4

FIGURE 5-17. For inhaled corticosteroids, both their average recommended daily doses (A; [258]) and their estimated daily doses causing 10% (CS10 ) and 20% (CS20 ) AUC cortisol suppression (B; [273]) are closely related to the relative receptor-binding affinities (rRBA) [279]. Data are for compounds denoted as follows: FLU, flunisolide (5-40); BUD, budesonide (5-41); FP, fluticasone propionate (5-44); TAA, triamcinolone acetonide (5-46); and BDP, beclomethasone dipropionate (5-48), which is the only exception, as it is inactive and is a prodrug that has to be transformed into the active 17␣-monopropionate (BMP, whose rRBA value was used in the figure).

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because of concerns about side effects, including possible local side effects, such as oral candidiasis or dysphonia [267], and in a few cases, even severe systemic side effects have been seen, especially when coadministered with CYP3A4 inhibitors [262,268–271]. In many patients, asthma and rhinitis are often treated simultaneously with different corticosteroids, a further potential cause of unexpected side effects. Finally, it is also important to note that whereas the dose–response curves for the therapeutic effects of inhaled corticosteroids are relatively flat, as they tend to be highly efficacious even at low doses, the dose–response curves for their side effects tend to be steep (an observation that was especially evident for fluticasone propionate) [272], so that larger doses (e.g., due to uncontrolled repeated administrations) can quickly become dangerous. Because most of these potential adverse effects usually arise only following long-term treatment, clinical studies of corticosteroids in the development phase monitor suppression of the hypothalamic–pituitary–adrenal (HPA) axis as a surrogate marker. Cortisol (hydrocortisone) suppression is also closely rRBA-related, as Figure 5-17B, showing the estimated daily doses causing 10% and 20% AUC cortisol suppression, clearly illustrates (data are for MDI formulations except for budesonide, which is for DPI [273]). The estimated average GR binding affinity of hydrocortisone, its endogenous ligand (cortisol, 5-38), is about 70 nM (Table 5-1), a reasonable value within the normal plasma concentration range of cortisol. Normal human plasma cortisol levels fluctuate between approximately 100 and 800 nM (being higher in the morning); however, free cortisol represents only about 5% of the total (i.e., 5 to 40 nM), as 80 to 90% is bound to corticosteroid-binding globulin (CBG, or transcortin) and the remainder is associated with albumin [274]. CBG is a transport/cargo glycoprotein that binds biologically active steroids with considerably higher affinity and specificity than other plasma proteins [275]. Unfortunately, CBG binding data have frequently been used as steroid “benchmark” binding data for QSAR studies [276–278], but there seems to be no close correlation between CBG binding and GR binding, which is the one that determines glucocorticoid activity. In a recent comprehensive quantitative analysis of rRBA data for GR obtained from more than 100 corticosteroid structures [279], we found a clear biphasic size dependence well described by a linearized biexponential (LinBiExp) model [100,280]. This model uses a novel functional form consisting of the logarithm of the sum of two exponentials to obtain a general bilinear functionality:
  ␦␸ I␸ y = f (x) = ␩ln e␣1 (x−␰c )/␩ + e␣2 (x−␰c )/␩ + ␹ +

(5.10)

␸

This completely general bilinear model uses a total of five unrestricted parameters (denoted here with Greek symbols): two slopes (␣1 and ␣2 ), a constant (␹ ) for shifting along the y (vertical) axis, a constant (␰ c ) for shifting along the x (horizontal) axis (i.e., to position the rate-change point), and a parameter (␩) for adjusting the smoothness or abruptness of the transition between the two linear portions [100,280]. For the glucocorticoid data, it was used to fit the binding free-energy (
G0 ) data with molecular size (volume) as the independent variable (x = V) [279] in the following

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SOFT DRUGS

form, which can also account for various substitution effects (␸i , i = 1, … , n) through the incorporation of indicator variables, I ␸i : n
 
G 0 (kJ/mol) = RT 0 ln K D = ␩ ln e␣1 (V −␯c )/␩ + e␣2 (V −vc )/␩ + ␹ + ␦␸i I␸i i=1

(5.11) For steroids that satisfy the main binding criteria at the GR (a representative sample of which is summarized in Table 5-1), this model can account for close to 80% of the variability in the free energy of binding
G0 (or log rRBA) data by using only two descriptors: calculated molecular volume and an indicator variable for the presence of 6␣/9␣-halogen or cyclic 16,17-acetal moieties (Figure 5-18). Accordingly, binding is strongest for corticosteroids close to an ideal size that is large enough to provide nonspecific (van der Waals type) interactions that are as large as possible, but is not too large to have difficulty fitting due to size limitations at the binding site. Binding

FIGURE 5-18. Glucocorticoid receptor (GR) binding of corticosteroids that satisfy the main binding criteria at the GR, shown as a function of the molecular size calculated (molecular volume, V), fitted using the LinBiExp bilinear model [eq. (5.11)] [279]. Free energies of binding (
G = RT ln K D ) or a corresponding log relative receptor binding affinity (rRBA) are shown on the vertical axis (the more negative the
G0 , the higher affinity the binding). 6␣- or 9␣-Halogenated (mostly fluorinated) or cyclic 16,17-acetal containing highly active structures are denoted separately with closed symbols; four antiprogestins with a somewhat different structural framework that were not used for fitting are shown as dashed open circles.

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affinity is dramatically increased by 6␣- or 9␣-halogenation or introduction of a cyclic 16,17-acetal moiety (on average about sevenfold), but there is no significant increase after the first substitution. Known highly active glucocorticoids, such as betamethasone 17-monopropionate, fluticasone propionate (5-44), and mometasone furoate (5-45), indeed satisfy both of these criteria. For small-enough structures, the size dependency (slope) of the free energy of binding obtained suggests that as long as only nonspecific interactions are involved, addition of a methylene-sized nonhydrogen atom to the ligand structure increases
G0 on average by about 1.5 kJ/mol, corresponding to an almost doubling of the binding affinity (101.5/5.708 = 1.83) [279]. This is somewhat less than some earlier general estimates for ligand–receptor binding (e.g., 3 to 6 kJ/mol per atom [281,282]), but it is quite reasonable, as here it represents only increases due to nonspecific interactions, whereas in general cases, size also incorporates a term due to specific interactions (and/or other interceptrelated assumptions) [281–283]. The present observation of an average doubling with addition of a methyl-sized group is also in excellent agreement with the average binding increment seen for 18 highly optimized inhibitors at five different protein targets in fragment-based drug discovery [284]. This found that a 10-fold increase in binding (i.e., a one-unit change in pK D or log K D ) requires an average increase of about 64 in molecular mass [284]; hence, the 1.8-fold increase seen here for addition of a non-hydrogen atom would require an increase of about 64 × log 1.8 ≈ 16 in molecular mass, about the mass of a methylene-sized unit. At first sight it might seem somewhat surprising that a nonspecific parameter such as size can account for such a large proportion of the binding affinity, as one would expect more specific, pharmacophore-related effects; however, the compounds included in the present study already satisfy the main structural requirements of binding at the GR, and they already contain all essential pharmacophores required for adequate binding [233,285]. Therefore, this set probably represents a sort of upper limit of binding; inactive steroids lacking various required pharmacophores were omitted. The crystal structure of the human GR ligand-binding domain (LBD) bound to dexamethasone, which was determined by using a receptor with a single point mutation (F602 S) [245], is in excellent agreement with the observations of the LinBiExpbased study. GR seems to have an additional branch, compared to the steroid-shaped pocket of androgen, estrogen, or progestin receptors, and can accommodate the larger 17␣ substituents of glucocorticoids that are not present in testosterone, estrogen, or progesterone [243,245]. Unspecific (van der Waals type) interactions within the surprisingly large 17-position side pocket of GR, where most of the ligands’ structural variations are (Figure 5-18), could be responsible for a good portion of the
G0 (rRBA) variation seen, and they could indeed be mainly size dependent. An overlap of representative, highly active glucocorticoid structures within the LBD of the human GR generated using the crystal structure complexed with dexamethasone [245] illustrates this point well: Even the largest structure (ciclesonide active metabolite) encounters only minimal structural bumps even though the structure of the binding pocket was obtained with the much smaller dexamethasone as ligand (Figure 5-19). Proteins are flexible structures in constant motion between different states of similar energies; hence, binding-site shape and size is, at least to some extent, determined by

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FIGURE 5-19. Overlap of the three-dimensional structures for several representative, highly active glucocorticoids within the ligand-binding domain of the human GR generated [279] using the crystal structure of GR complexed with dexamethasone [245]. The following structures are included: beclomethasone monopropionate, orange; 22R budesonide (5-41), brown; ciclesonide active metabolite, green; clobetasol, purple; dexamethasone, black; fluticasone propionate (5-44), blue; mometasone furoate (5-45), red. (See insert for color representation of the figure.)

the ligand [286], and the larger ligands almost certainly fit even better than indicated by this figure, especially that the LBD of GR has been shown to be particularly flexible [246,247], and other members of this receptor superfamily also show unique plasticity (e.g., SERM binding at estrogen receptors [287]). As long as the substituents are not prohibitively large, increasing size tends to result in increasing binding. For the GR, optimum receptor binding is predicted to occur somewhere around V max = 361.0 Å3 , which is very close to those of highly active steroids such as clobetasol propionate (5-43; 349.9 Å3 ), fluticasone propionate (5-44; 357.8 Å3 ), or mometasone furoate (5-45; 367.9 Å3 ). Dexamethasone (5-42), which was observed to occupy only part of the volume of the overall GR steroid pocket but still have nearly every atom of its steroid core in contact with one or more hydrophobic residues of GR in addition to specific protein–ligand hydrogen bonds at all hydrophilic moieties (e.g., at the C3 ketone, at the 11␤-, 17␣-, and 21-hydroxyl) [245], indeed has a considerably smaller volume (298.0 Å3 ). Obviously, overall size is not a perfect descriptor, and, for example, size limitations are certainly not the same in all regions of the LBD of GR (e.g., they are probably more stringent at the 17␤ than at the 17␣ side chain), and this is probably why the larger compounds are much more scattered from a linear fit than are the smaller ones. Most substituted 17␤ esters, such as ciclesonide (5-47) or beclomethasone dipropionate (5-48), are inactive but can be activated by hydrolytic cleavage of the ␤ ester moiety. Deacylcortivazol, a highly potent nonfluorinated glucocorticoid that contains a phenylpyrazole replacement at

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the conserved 3-ketone of steroid hormones (rRBA = 4000) [247], normally required for activation of their cognate receptors, fits the size-predicted pattern without the downward curvature, and it indeed has been shown to increase considerably the size of the GR ligand binding pocket [247]. Regarding the halogenation effects, an activity-enhancing effect for fluorination has been long known for glucocorticoids. One of the earliest detailed thermodynamic analyses of GR binding [288], mainly on the basis of data for hydrocortisone (5-38) and prednisolone (5-39) derivatives, suggested an approximate threefold increase for 9␣-fluorination (

G0 = –2.39 kJ/mol), twofold increase for 6␣-fluorination (–1.51 kJ/mol), and fourfold increase for 9␣-chlorination (–2.97 kJ/mol) in RBA vs. the average of the six- to sevenfold increase obtained here (–4.6 kJ/mol). However, this study by Wolff and co-workers used a limited number of structures and did not consider separately the fluorination of cyclic 16,17-acetal compounds, where its activityenhancing effect seems to be much less. It is still unclear why 6␣- or 9␣-halogenation increases GR binding so significantly, especially since the H → F replacement is a classic isosteric replacement often used to provide metabolic stability because of the stability of the C− −F bond and the steric similarity between H and F [289]. There seems, however, to be a clear size limitation: bromination already decreases activity [288]. The crystal structure of the dexamethasone–receptor complex does not seem to indicate the presence of any special interaction. Interestingly, contrary to the glucocorticoid case, 6␣-halogenation (F, Cl) seems to have no effect on the binding of progesterone to its receptor [290,291]. Also, whereas 6␣ or 9␣ fluorination seems to have about the same effect on GR binding, they seem to have very different effects at the mineralocorticoid receptor, substitution at 9␣ being much more effective than at 6␣, at least in the case of fluoroprednisolone [292]. Interestingly, the H/F replacement also causes about a fivefold change in the binding of two thrombin inhibitors [293, 294]. Fluorination may also cause metabolism or pharmacokinetic changes, as, for example, oxidation of steroids by 11␤-hydroxysteroid dehydrogenase (11␤-HSD) is diminished if they are 6␣- or 9␣-fluorinated [295]. However, overall human half-lives (t1/2 ) are not very different for representative cases such as dexamethasone 4.0 h vs. prednisolone 3.2 h (p.o.) [296] or flunisolide ca. 1.5 h vs. budesonide ca. 3.0 h (i.v., inh.) [235,297]; hence, this effect, if present at all, is much less relevant than the one produced on GR binding affinity. Even when corticosteroids are applied only topically (e.g., lung, nasal mucosa, gastrointestinal tract, eye, or skin), significant portions can still reach the general circulatory system, and the resulting systemic side effects that have been mentioned before, together with local side effects, often limit their use. For example, topical corticosteroids represent an important class of drugs used to treat ocular inflammations and allergies, as they are the most effective ocular anti-inflammatory compounds and offer the broadest range of treatment; however, their usefulness is severely limited because they can also produce a number of ocular complications such as IOP elevation and resulting steroid-induced glaucoma, induction of cataract formation, and secondary complications [298]. Ocular administration of corticosteroids usually produces increased IOP as a result of increased resistance to aqueous humor outflow, but the precise mechanism of decreased outflow is not known [299]. The mechanism

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FIGURE 5-20. Mechanism of steroid-induced cataract according to the most prominent hypothesis [302–305]. The process involves, first, the formation of Schiff bases between the steroid C20 ketone group and nucleophilic groups such as ε-amino groups of lysine residues of proteins, and then a Heyns rearrangement involving the adjacent C21 hydroxyl group, which results in stable amine-linked adducts.

of steroid-induced cataract is also not entirely clear [300], but the most prominent hypothesis involves the formation of Schiff bases between the steroid C20 ketone group and nucleophilic groups such as ε-amino groups of lysine residues of proteins (Figure 5-20). Schiff base formation is potentially followed by a Heyns rearrangement [301] involving the adjacent C21 hydroxyl group and affording stable amine-linked adducts [302–305]. This covalent binding results in destabilization of the protein structure, allowing further modifications (i.e., oxidation) and leading to cataract. Corticosteroids are also subject to different oxidative and/or reductive metabolic conversions, and formation of various steroidal metabolites can lead to undesirably complex situations as illustrated by the metabolism of hydrocortisone (5-38) (Figure 5-21). A considerable number of attempts were aimed to improve this situation, and SD approaches are particularly well suited for this purpose (Figure 5-1). Finally, it has to be mentioned that there is a frequent misconception regarding SDs, and in particularly soft steroids, that has to be clarified. Often, the soft nature is associated with fast hydrolytic degradation, but this is not necessarily so. If hydrolysis is too rapid, only weak activity may be obtained. The desired increase of the TI can be achieved only if the drug is sufficiently stable to reach the receptor sites at the target organ and to produce its desired effect, but the free non-protein-bound drug undergoes facile hydrolysis to avoid unwanted, systemic side effects. To separate the desired local activity from systemic toxicity successfully, an adequate balance has to be achieved among intrinsic activity, solubility/lipophilicity, tissue distribution, protein binding, and rate of metabolic deactivation. In the case of slow, sustained release to the general circulatory system from the delivery site, even a relatively slow hydrolysis could result in a very low, almost steady-state systemic concentration. Cortienic Acid–Based Soft Steroids: First Generation (Loteprednol Etabonate and Analogs) Loteprednol etabonate [5-67, LE; (11␤,17␣)-17((ethoxycarbonyl)oxy)11-hydroxy-3-oxoandrosta-1,4-diene-17-carboxylic acid chloromethyl ester; Figures 5-22 to 5-24] is an active corticosteroid that lacks serious side effects and that received final FDA approval in 1998 as the active ingredient of two ophthalmic preparations, Lotemax and Alrex [306–309]. Currently, it is the only corticosteroid

OH

O

O

H

OH

HO

H

OH

H

OH

HO

5-61 cortols

HO

OH

HO

HO

5-58 cortolones

HO

O

OH

HO

OH

O

5-62 tetrahydrocortisol

HO H

5-63 cortolic acid

121

FIGURE 5-21 Summary of the major metabolic pathways [321] of hydrocortisone (cortisol; 5-38, also 1-8). Structural changes in each metabolite compared to the parent hydrocortisone are indicated in blue.

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H

HO

O

OH

HO

5- 60

O

OH

5-57 cortolonic acid

OH

HO

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OH

OH

O

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O

OH

5- 59

OH

5-64 cortisolic acid

O

HO

O

OH

HO

5-38 hydrocortisone (cortisol)

O OH

OH

HO

OH

HO

HO

5-56 tetrahydrocortisone O

O

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OH

O

H

OH

5-53 cortisone

O

OH

OH O

O

5-54 cortisonic acid

O

5-65 cortienic acid

O

O OH

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O

HO

5-51 O

OH

OH

O

OH

HO

OH

O

O

O

HO

5-52 6-hydroxycortisol

O

OH

O

OH

HO

5-55 11-dehydro cortienic acid

O

OH

O

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FIGURE 5-22. Inactive metabolite (5-65)–based design of first- (5-66) and secondgeneration (5-70) cortienic acid–based soft steroids and their selected representative compounds loteprednol etabonate (LE, 5-67) and etiprednol dicloacetate (ED, 5-71), respectively.

approved by the FDA for use in all inflammatory and allergy-related ophthalmic disorders, including inflammation after cataract surgery, uveitis, allergic conjunctivitis, and giant papillary conjunctivitis. It was later also approved as part of a combination ophthalmic suspension with an anti-infective agent (Zylet, loteprednol etabonate 0.5%/tobramycin 0.3%) as well as an ophthalmic ointment (Lotemax

FIGURE 5-23. Main inactivating metabolic pathways of loteprednol etabonate (5-67) and etiprednol dicloacetate (5-71), both leading ultimately to the formation of
1 -cortienic acid (5-69,
1 5-65; M-CA).

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123

FIGURE 5-24. Three-dimensional overlapping pharmacophore structures of etiprednol dicloacetate (5-71) (in lighter colors) with loteprednol etabonate (5-67) (left figure) and fluticasone propionate (5-44) (right figure). Both views are from the ␤ side, from slightly above the steroid ring system. (See insert for color representation of the figure.)

ointment 0.5%). LE resulted from a classic inactive metabolite-based SD approach [10,235,308,310–320]. As mentioned, hydrocortisone (5-38) undergoes a variety of oxidative and reductive metabolic conversions (Figure 5-21) [321]. Oxidation of its dihydroxyacetone side chain leads to the formation of cortienic acid (5-65) through a 21-aldehyde (21-dehydrocortisol) and a 21-acid (cortisolic acid, 5-64). Cortienic acid is an ideal lead for the inactive metabolite approach because it lacks corticosteroid activity and is a major metabolite excreted in human urine. To obtain active compounds, the important pharmacophores found in the 17␣ and 17␤ side chains had to be restored (Figure 5-22). Suitable isosteric or isoelectronic substitution of the ␣-hydroxy and ␤-carboxy substituents with esters or other types of functions should restore the original corticosteroid activity and also incorporate hydrolytic features to help avoid the accumulation of toxic levels. More than 120 of these first-generation soft steroids (5-66) that resulted from modifications of the 17␤ carboxyl function and the 17␣ hydroxy function together with other changes intended to enhance corticosteroid activity [e.g., introduction of
1 , fluorination at 6␣ (X6 ) and/or 9␣ (X9 ), methylation at 16␣ or 16␤ (R16 )] have been synthesized. The first soft analogs of this type (5-66) were synthesized during the late 1970s soon after the introduction of the SD concept [1], followed by a systematic synthetic study performed in collaboration with the Otsuka Pharmaceutical Company in Japan in 1980–1981 [3,310,322]. A haloester in the 17␤-position and a novel carbonate [312] or ether [323] substitution in the 17␣-position were found as critical functions for activity. Incorporation of 17␣ carbonates or ethers was preferred over 17␣ esters to enhance stability and to prevent the formation of mixed anhydrides that might be produced by reaction of a 17␣ ester with a 17␤ acid functionality. Such mixed anhydrides were assumed to be toxic and probably cataractogenic (Figure 5-20). The carbonates were expected to be less reactive than the corresponding esters, due to the lower electrophilicity of the carbonyl carbon. A variety of 17␤ esters were synthesized. Because this position is an important pharmacophore that is sensitive to small modifications, the freedom of choice was relatively limited. For example, although

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CPM (%)

100

LE DEX M-CAE M-CA

50

0

0

2 Log Conc (nM)

4

FIGURE 5-25. Receptor-binding data of dexamethasone (5-42), LE (5-67), and its metabolites (5-68: M-CAE, 5-69: M-CA) at the glucocorticoid receptor (in the presence of cortienic acid) normalized and fitted with standard binding curves (GraphPad Prism) (data from [312]).

chloromethyl or fluoromethyl esters showed very good activity, the chloroethyl or ␣-chloroethylidene derivatives demonstrated very weak activity. Simple alkyl esters also proved virtually inactive. Consequently, the 17␤ chloromethyl ester was held constant and 17␣ carbonates with different substituents on the steroid skeleton were varied for further investigation. Binding studies using rat lung cytosolic corticosteroid receptors showed that some of the newly synthesized compounds approach and even exceed the binding affinity of the most potent corticosteroids known [279,285]. LE itself has a binding affinity that is higher than that of dexamethasone (Figure 5-25) [279,312]. It has a relative receptor-binding affinity (rRBA) of 150, which is among the highest among nonfluorinated glucocorticoids, as it has a volume close to the ideal one for binding to the glucocorticoid receptor GR (Figure 5-18). LE, and some of the other soft steroids, provided a significant improvement of the therapeutic index (TI) determined here as the ratio between the anti-inflammatory activity and the thymus involution activity (Figure 5-26) [308,324,325]. In traditional corticosteroids, efficacy and safety tend to run in parallel, as already illustrated in Figure 5-17; hence TI, which represents the ratio between efficacy and toxicity (TI = TD50 /ED50 ), is essentially unchanged regardless of intrinsic activity. This is well illustrated again by Figure 5-26, which shows the good correlation (r2 = 0.996) between efficacy, measured via ED50 , the median effective dose for the anti-inflammatory activity in the cotton pellet granuloma test (␮g/pellet), and toxicity, measured via TD50 , the median toxic dose determined from the thymolysis potency (␮g/pellet), for a number of traditional corticosteroids [182]. Compared to them, however, LE shows a clear, about 20-fold improvement in TI due to its improved safety (reduced toxicity). Loteprednol etabonate (LE; 5-67) was selected for development based on various considerations, including the TI, availability, synthesis, and “softness” (the rate and easiness of metabolic deactivation). Early studies in rabbits [313,316] and rats [317]

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125

-4.5

Cpd.

TI TD50/ED50 56.2 3.8 3.1 2.5

log 1/TD 50 (μg/pellet)

-4.0

LE CBp HCb BMv

-3.5 -3.0

Rel. TI TI/TIBMv 22.5 1.5 1.3 1.0

-2.5

LE

HCb BMv

y = 0.916x -0.613 r 2 = 0.996

-2.0 -1.5

Improved TI for LE (approx. 20×)

CBp

-1.0 -0.5

0.0

-0.5

-1.0

-1.5

-2.0

-2.5

log 1/ED 50 (μg/pellet)

FIGURE 5-26. Improved therapeutic index, TI, of the soft corticosteroid LE compared to other, traditional corticosteroids. Because in corticosteroids, efficacy and safety trend to run in parallel (see also Figure 5-17), TI which represents the ratio between efficacy and toxicity (TI = TD50 /ED50 ), is essentially unchanged regardless of intrinsic activity. This is well illustrated here by the relationship between efficacy, measured via ED50 , the median effective dose for the anti-inflammatory activity in the cotton pellet granuloma test (␮g/pellet), and toxicity, measured via TD50 , the median toxic dose determined from the thymolysis potency (␮g/pellet) for steroids such as hydrocortisone 17␣-butyrate (HCb, 0.1%), betamethasone 17␣-valerate (BMv, 0.12%), and clobetasone 17␣-propionate (CBp, 0.1%) [182]. Loteprednol etabonate (LE, 0.1%), however, shows a clear, about 20-fold improvement.

demonstrated that consistent with its design, LE is indeed active, is metabolized into its predicted metabolites (5-68, 5-69, Figure 5-23), and these metabolites are inactive (Figure 5-25) [312]. The PK profile of LE indicated that when absorbed systemically, it is rapidly transformed to the inactive metabolite 5-68 (Figure 5-27) and eliminated from the body mainly through the bile and urine [317,318,320]. In rats, the metabolism and excretion of LE was found to be dose-dependent [320]. As the i.v.-administered dose increased from 1 mg/kg to 20 mg/kg, the half-life (t1/2 ) increased from 16 min to 49 min, and the total clearance (CLtot ) per kilogram decreased from about 120 mL/min to 60 mL/min; however, all these values are higher than the physiological hepatic blood flow in rats (58 mL/min per kilogram), indicating quick elimination and metabolism, especially at smaller concentrations, such as those that can be expected following topical applications [320]. In rats, the PK of the metabolites (5-68, 5-69) has also been characterized in detail [326,327]. In dogs, after i.v. administration of the relatively high dose of 5 mg/kg, loteprednol etabonate had a terminal half-life of 2.8 h, a mean residence time of 1.7 h, and a total clearance of about 1 L/h·kg [318]. LE showed a plasma protein binding >90% and, according to limited studies in dogs and rats, a very low oral bioavailability of close to 0% [318]. In dogs, both

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Concentraon (µg/mL)

10

LE

M-CAE

1

0.1

0.01 0

20

40

60

80

100

120

140

160

Time (min)

FIGURE 5-27. Blood concentration time profiles of LE (5-67) and its main metabolite M-CAE (5-68) in rats (n = 3) after i.v. administration of a 20-mg/kg dose. The pharmacokinetics of LE could be well described by a two-compartment model with a clearance of CL = 60.4 mL/min·kg, corresponding to a terminal elimination half-life of t1/2,␤ = 48.8 min [320].

oral and i.v. administration resulted in over 90% excretion, mostly as acidic metabolites in feces, an observation supporting the facile elimination of the metabolites to the bile. LE did not affect the IOP in rabbits [316] an observation confirmed later in various human studies (Figure 5-28) [328]. Consistent with the soft nature of this steroid, systemic levels or effects could not be detected in humans even after chronic ocular administration [329]. Plasma levels of loteprednol etabonate and its primary 8

Paent %

6

4

2

0

0.99%

0.64%

6.71%

Placebo

LE

PA 1%

n = 304

n = 624

n = 164

FIGURE 5-28. Percent of patients with IOP elevation greater than 10 mmHg among patients not wearing contact lenses and treated for more than 28 days (pooled data). The number of patients within each group was as follows: placebo, n = 304; loteprednol etabonate (LE; 0.2% and 0.5%), n = 624; prednisolone acetate (PA; 1.0%), n = 164 [328].

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70

Placebo

127

LE 0.5%

% Resolved

60 50 40 30 20 10 0 2 (day 2-6)

3 (days 7-12)

4 (days 13-20)

Final

Visit

FIGURE 5-29. Resolution of anterior chamber inflammation (sum of cell and flare scores) at each visit during the treatment period in postcataract inflammation with intraocular lens implantation [182].

metabolite (5-68) were below the 1 ␮g/L detection limit in 10 healthy volunteers who received the drug (one drop, 0.5%) in both eyes eight times daily for 2 days and four times daily for an additional 41 days [329]. LE has been shown to be a safe and clinically effective treatment for contact lens–associated giant papillary conjunctivitis, seasonal allergic conjunctivitis, postoperative inflammation (Figure 5-29), uveitis, and dry eye syndrome [306,307,309]. A retrospective study confirmed that in agreement with its design principles, even long-term use (>12 months) of LE caused no reported adverse effects [330]. An additional recent work [331] further demonstrated the outstanding safety profile of LE. In these studies, 30 “steroid-sensitive” patients who have undergone corneal transplantation were treated, as usual, with prednisolone acetate. This treatment caused an average elevation of intraocular pressure (IOP) to 31.1 mmHg. Increased IOP after corneal transplant can lead to irreversible vision loss through optic nerve damage. To avoid this, the patients were switched to LE (Lotemax). The IOP in all cases was reduced during the 21 weeks of treatment to an average of 18.2 mmHg (Figure 5-30). It was concluded that switching from prednisolone acetate to LE in known steroid responders was successful in reducing IOP and did not increase the risk of allograft rejection [331]. LE in combination with tobramycin was also found to be effective in the treatment of ocular inflammation associated with blepharokeratoconjunctivitis [332], while significantly less likely to produce elevations in IOP than the dexamethasone/tobramycin combination [333]. On the basis of promising results from animal studies [308,319,320,334], LE is also being developed for treatment of asthma, rhinitis, colitis, and dermatological problems; relevant results have been reviewed recently [235]. LE might also be a promising agent for localized immunosuppression: for example, in pancreatic islet transplantations using a biohybrid device as a bioartificial pancreas [335–337]. LE nasal spray for treatment of allergic rhinitis has already been evaluated in a number

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Mean IOP (mmHg)

30 25

Elevated IOP

35

31.1

20

18.2

15 10 5 0

PA 1.0% (baseline) (n = 30)

LE 0.5% (final visit) (n = 30)

FIGURE 5-30. Mean intraocular pressure (IOP) of post-corneal transplant patients treated with prednisolone acetate (PA) and then switched to loteprednol etabonate (LE). “Steroidsensitive” patients (n = 30) treated with prednisolone acetate following corneal transplantation were switched to LE when their IOP levels became too high (average: 31.1 mmHg). During the 21-week treatment, IOP was reduced to an average of 18.2 mmHg [331].

of clinical studies, and they indicate that LE nasal spray (400 and 800 ␮g once daily) could be a safe and well-tolerated treatment for up to two weeks for allergic rhinitis (Figure 5-31) [338,339]. A large (n = 165) environmental exposure unit study in patients with seasonal allergic rhinitis also confirmed that LE 400 ␮g once daily is effective and superior to placebo. After 14 days of treatment, patients in this LE group had significantly lower total nasal symptom scores than those of patients receiving placebo (p = 0.007) [340]. Cortienic Acid–Based Soft Steroids: Second Generation (Etiprednol Dicloacetate and Analogs) Following the success of LE, a new, second generation of soft steroids with a unique 17␣-dichloroester substituent has also been developed (5-70, Figure 5-22) [235,341–344]. This is a unique design: no known corticosteroid contains halogen substituents at the 17␣ position. Nevertheless, the pharmacophore portions of these second-generation soft steroids, including the halogen atoms at 17␣, can be positioned so as to provide excellent overlap with those of the traditional corticosteroids (Figure 5-24) [285]. Dichlorinated substituents seem required for activity and a sufficiently soft nature, and two justifications seem likely. First, with dichlorinated substituents, one of the Cl atoms will necessarily point in the direction needed for pharmacophore overlap, but with monochlorinated substituents, steric hindrance will force the lone Cl atom to point away from this desired direction. Second, whereas dichloro substituents increase the second-order rate constant kcat /K M of enzymatic hydrolysis in acetate esters by a factor of about 20 compared to the unsubstituted ester, monochloro substituents do not cause any change [61].

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25 Median Cortisol (µg/dL)

Baseline 20 15 10 5 0

0

2

4

6

8

10

12 14 Hour

16

18

20

22

24

16

18

20

22

24

25 Median Cortisol (µg/dL)

Day 43 20 15 10 5 0

0

2

4

6

LE 400

8

10

LE 800

12 Hour

14

PLA-PLA

PLA-DEX

FIGURE 5-31. Median cortisol profiles obtained in a randomized double-blind placebo- and active-controlled single-center trial with four parallel groups designed to investigate the effects of glucocorticoid nasal sprays on the HPA axis function, as well as safety, general and local tolerability, and PK in patients (n = 80) with perennial allergic rhinitis in a six-week study.

In this class of soft steroids, contrary to the first class, hydrolysis cleaves primarily not the 17-␤-position but the 17␣-position ester (Figures 5-23 and 5-32). Nevertheless, the corresponding metabolites are also inactive. From this series, etiprednol dicloacetate (ED; 5-71, Figures 5-22 and 5-23) was selected for development. ED has shown better receptor binding affinity than LE, and was proven as, or even more effective, than budesonide (BUD) in various asthma models (Figure 5-34). In agreement with its soft nature, ED was found to have low toxicity in animal models and in human clinical trials [341–343,345]. The transrepressing and transactivating activity of ED and BUD were compared by measuring their inhibition in interleukin (IL)-1␤ production of a stimulated human monocyte cell line and by evaluating glucocorticoid-induced increase in the activity of tyrosine aminotransferase of a rat hepatoma cell line, respectively [343]. ED was found to be a dissociated

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100 90

Concentraon (μM)

80 70 60 ED 50

M-CA, Et

40

M-CA

30 20 10 0 0

4

8

12

16

20

24

Time (h)

FIGURE 5-32. Metabolism of ED (5-71) in rat plasma into its main (inactive) metabolites M-CA,Et (5-72) and M-CA (
1 -cortienic acid, 5-69).

Transrepression

250

150

Transacvaon Dex

Dex

Bud

Bud

ED

ED 50

50

150

250

FIGURE 5-33. Comparison of the transrepression (anti-inflammatory) and transactivation (carbohydrate metabolism-altering) effects of etiprednol dicloacetate (ED, 5-71), budesonide (Bud, 5-41), and dexamethasone (Dex, 5-42 used as reference) [343]. Transrepressing activities were determined using the human monocytic cell line, THP1. Cells were stimulated with LPS and silica in the presence of different concentrations of the steroids, and the production of IL-1␤ was measured. Transactivating activities were determined by measuring the steroidinduced increase in the activity of tyrosine aminotransferase (TAT, which is involved in the glucocorticoid-dependent stimulation of neoglucogenesis) using the rat hepatoma cell line (HTC, ECACC 93129198). The activity of dexamethasone was taken as 100% at each molar concentration, and relative activities were calculated for the other steroids. Averages ± SD of three experiments for concentrations of 0.1 ␮M are shown.

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100

ED50 = 0.62 µ µg/kg

ED50 = 1.61 µ µg/kg

Eosinophil number / visual field

80

*** ***

60 *** ***

40

***

***

10

100

*** ***

20

0 Sens.

Chall.

0.1

1.0

10

E
prednol dicloacetate ED (µg/kg)

100

0.1

1.0

Budesonide BUD (µg/kg)

FIGURE 5-34. Effect of etiprednol dicloacetate (ED) and budesonide (BUD) on antigeninduced airway eosinophil infiltration into the bronchoalveolar tissues of brown Norway rats (peribronchial eosinophil numbers shown; n = 4 to 25 per treatment group). Sensitized rats were treated intratracheally with different doses of the drugs (0.1, 1, 10, and 100 ␮g/kg) and 2 h later were challenged with ovalbumin aerosol. The level of significance (Mann–Whitney U test) in case of each drug-treated group compared with vehicle-treated challenged controls was p < 0.001.

glucocorticoid: to possess reduced transactivating activity with a preserved transrepressing activity (Figure 5-33). Transactivation is mediated by binding of the hormone-activated receptor to a defined DNA sequence, called glucocorticoid response element (GRE). This process may account for some of the unwanted effects of glucocorticoids via the increase in expression of genes involved in gluconeogenesis and development of arterial or ocular tensions. Transrepression, which seems to be the main mechanism by which glucocorticoids suppress inflammation, may be the result of binding to negative GREs, but it occurs mainly by interaction with transcription factors (AP-1 and NF-␬B), which control the genes of many inflammatory mediators, from IL-1␤ to RANTES (regulated upon activation, normal T-cell expressed and secreted) chemokine. Hence, the dissociation of transactivating and transrepressing activity seen for ED is a likely advantage that may further help in separating the beneficial anti-inflammatory activity from the undesired side effects, and is in line with the development of dissociated steroids, one of the novel mechanistic approaches pursued in development of new inhaled corticosteroids [346,347]. Fluocortin Butyl Fluocortin butyl (5-73, Figure 5-35) is an anti-inflammatory steroid obtained in one of the early approaches aimed at integrating ester moieties into steroid structures. Metabolism studies on fluocortolone revealed a number of

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O O

O O O

O

O

O

O HO

F

O HO

itrocinonide

F

O

F

5-74

O

fluocortin butyl

O

O

5-73 O

O H

O HO

S

5-75

O

F

F

O O O S

H

HO

O

OH

O

O

O O

HO

O

O OH

HO

O

F

Cl

5-76

O

F

5-77

O

5-78

O

F

FP16CM

F

HO

O

O O O

O

O

O

HO

()

O

HO

HO

O

OH

Cl

O

O

n

O

O

R

HO

OH

OBz

O

F

F 5-79 O

5-80

O

FDP16CM-ibuprofen

5-81 O O HN

O

O

O R R = H, Me

O

O O

S O

OH

HO

OH HO

F O

OH O

F 5-82

5-83 O

FIGURE 5-35. Soft corticosteroids and related structures discussed in the text.

oxidative and reductive metabolites in human urine [348], including fluocortolone21-acid, an inactive metabolite. Synthesis and pharmacological evaluation of its different ester derivatives yielded fluocortin butyl (5-73; Vaspit, Novoderm, Varlane), the butyl ester of a C21 carboxy steroid [349–352]. The ester is an anti-inflammatory agent of rather weak activity, and any portion absorbed systemically following topical application is hydrolyzed into inactive species. The widespread use of this steroid has been hindered by its low intrinsic activity. The glucocorticoid receptor affinity and the topical anti-inflammatory potency of fluocortin butyl (5-73) are several-fold lower than those of dexamethasone [350]. Fluocortin butyl ameliorated allergic rhinitis at daily doses of 2 to 8 mg divided into two to four daily inhalations [353,354], but it did not protect against bronchial

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obstruction in bronchial provocation tests, even at 8-mg doses divided into four daily inhalations, in contrast to a 10-fold lower dose of beclomethasone dipropionate (5-48) [355]. Itrocinonide A soft steroid series containing 17␤ methylcarbonate ester moieties susceptible toward hydrolysis was also developed during the 1980s at Astra [356]. Since unsubstituted 17␤ esters tend not to be very susceptible toward hydrolysis due to their steric hindrance, in these structures, the metabolically sensitive spot has been moved farther away along this side chain by introduction of a carbonate moiety (5-74, Figure 5-35). Receptor affinities varied significantly with the substituents and with the stereochemistry of the chiral center at the 17␤ ester. The double-fluorinated compound selected, itrocinonide (5-74), had a receptor affinity similar to that of budenoside and a sufficiently rapid rate of in vitro hydrolysis (t1/2 = 30 min in human blood at 37◦ C) [356]. The in vivo potency of itrocinonide was less than that of budesonide, but in agreement with SD design principles, the ratio between its anti-inflammatory efficacy in airways and lung and its systemic steroid activity (i.e., thymus involution or plasma cortisol suppression) was much better than the corresponding ratio for budesonide. It also had very good systemic tolerance in human volunteers and asthmatics. Owing to its short plasma half-live (ca. 30 min), which is about one-fifth of that of budenoside and fluocortin butyl, itrocinonide lacked measurable systemic glucocorticoid activity. In patients with asthma or seasonal rhinitis, itrocinonide administered as a dry powder formulation did exert some antiasthmatic and antirhinitic efficacy, but these effects were not sufficient to compete with the efficacy of current inhaled steroids [356]. Glucocorticoid ␥-Lactones A research group at GlaxoSmithKline has explored a series of various ␥ -lactone derivatives, including 21-thio derivatives of fluocinolone acetonide with ␥ -lactones and cyclic carbonates (e.g., 5-75) [357,358] and sulfur-linked ␥ -lactones incorporated at the 17␤-position (e.g., 5-76) [359]. For these compounds, human serum paraoxonase (EC 3.1.8.1) was claimed to be the metabolizing enzyme [357,360], which is of interest because this enzyme has a much lower activity in lung tissue than in plasma, and thus it can provide improved site-specific activity for inhaled compounds. Contrary to the corresponding esters, 21-thio-linked lactones were stable in human lung S9 preparation (t1/2 > 480 min for 5-75), but rapidly hydrolyzed in human plasma (t1/2 < 1 min) [357]. A higher metabolic stability of such structures in lung tissues than in corresponding plasma has been confirmed independently [356]. The rate of hydrolysis was also rapid in plasma for the 17-linked lactones possessing a sulfur in the ␣-position of the butyrolactone group (t1/2 < 5 min), whereas C-linked lactones were stable [359]. Among the compounds of this series, 5-76 showed promising topical anti-inflammatory activity in the rat ear edema model and much lower systemic effects than those of budesonide in the thymus involution test. Nevertheless, development of these series for asthma has been discontinued, probably because too rapid enzymatic inactivation at the desired site of action resulted in reduced anti-inflammatory efficacy [361].

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SOFT DRUGS

Soft 17-Furoate Androstadienes More recently, a SD design approach aiming toward a safe inhaled corticosteroid has also been reported by Sandham and coworkers at Novartis [362]. The starting point of this design was essentially a 6␣-F substituted mometasone furoate (5-45) analog with a 17␤ acid inactive metabolite. Hence, various 17␣ furoate ester substituents were explored while maintaining the 17␤ methyl esters substituent as the intended soft metabolic deactivation site. The structures obtained showed good (1 to 10 nM) affinity toward the glucocorticoid receptor. In vivo screening of the synthesized analogs in a rodent model identified 5-77 (Figure 5-35) as a promising lead with minimal oral absorption as well as superior efficacy and duration of action and a similar intratracheal (i.t.) side effect profile compared to budesonide [362]. Compound 5-77 showed complete inhibition of eosinophilia 24 h after 1-mg/kg dosing without any significant oral side effects. By comparing the structure of 5-77 to that of mometasone furoate (5-45), it is obvious that it can be considered as an intended soft analog of 5-45. It has to be mentioned, however, that even if these compounds were intended as soft drugs, they seem to be unusually stable in plasma, showing no significant extrahepatic metabolism. Compound 5-77 was found not to degrade in rat or human plasma over 30 min at 37◦ C, indicating that the C21 methyl ester, which is sterically strongly hindered in these structures, is stable to plasma esterases under these conditions. Antedrug Steroids A number of mostly prednisolone-based ester derivatives have been synthesized and investigated by Lee and co-workers at Florida A&M University in a series of attempts designated as “antedrug” designs [363–374]. As mentioned, the term antedrug is unfortunate, misleading, and should be avoided since despite being intended to denote the same concept as soft drug, it, in fact, implies the conceptual opposite: that is, a prodrug (since the Latin ante- prefix is very similar in meaning to the Greek pro- prefix, meaning prior to, or precedent, hence, indicating an inactive agent that has to be activated). These steroids were aimed to improve the localto-systemic activity ratio of anti-inflammatory steroids and may be considered as SD designs based on hypothetical inactive metabolites. Compounds studied include ester derivatives of steroid 21-oic acids [363], a number of 16␣-carboxylate analogs (e.g., 5-78) [364–366,369,371,375], 6-carboxylate analogs [367], and (16␣,17␣-d) isoxazoline derivatives [368,370,372]. Some of these compounds were found to have relatively low activity, similar to that of hydrocortisone or prednisolone, and they also achieved some, but not very significant, improvement in the local-to-systemic activity ratio. Relative receptor binding affinities (rRBAs) of two such 9␣-fluorinated steroids for the cytosolic glucocorticoid receptor are 11 and 4 for FP16CM (5-78) and its 21-acetate derivative FP16CMAc, respectively (Table 5-1) [279,371]. By comparison, loteprednol etabonate (5-67), a nonfluorinated soft steroid, has an rRBA of around 150 (Table 5-1) [279,312]. As an additional possible attempt to increase topical potency by a different approach, the 17␣-dehydro steroid FDP16CM was conjugated through an ester bond (5-79) to nonsteroidal anti-inflammatory drugs such as ibuprofen [376], an approach that could be qualified as essentially a pro-soft drug. Topical potency was not increased compared to the nonconjugated steroid (because of decreased receptor

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binding); however, the local-to-systemic activity ratio was still improved compared to that of prednisone. Other Corticosteroid Designs Several other groups also made attempts to separate local and systemic effects by integrating moieties susceptible to rapid nonhepatic metabolism within the corticosteroid structure. One of the more successful attempts explored 17␣-(alkoxycarbonyl)alkanoate analogs (5-80) of clobetasol propionate [377]. Again, this can be considered as a hypothetical inactive metabolite-based approach, and a corresponding metabolite (5-80, n = 2, R = H) has indeed been shown to be inactive. Esters that were susceptible to rapid hydrolysis exhibited good separation of topical anti-inflammatory to systemic activity. The study also indicated the existence of an optimal volume for the 17␣ side chain. For example, the methyl succinate derivative (5-80, n = 2, R = methyl) showed as potent topical anti-inflammatory activity as that of clobetasol propionate, but dramatically reduced thymolytic activity. Therefore, the corresponding TI was increased more than 130fold compared to clobetasol propionate. It has to be mentioned, however, that for this compound (5-80) as well as for the glucocorticoid ␥ -lactones (5-75, 5-76), active compounds may be formed from the inactive metabolite; for example, in the case of 5-80, chemical cleavage of the succinate ester to the active clobetasol can result in a possible active → inactive → active sequence, which should not be the case in a good SD design. Another effort involved the design, synthesis, and testing of a colon-targeted pro-soft drug (5-81) for possible oral treatment of ulcerative colitis [378]. These C20 oxyprednisolonate 21-esters contain glucopyranosyl ethers to render the proSDs hydrophilic and thus poorly absorbable in the small intestine. Removal of the glucopyranosyl ethers releases the corresponding active soft drug. This process is mediated by colonic bacteria within the colonic lumen, as demonstrated in vivo after administration in the jejunum of guinea pigs. In the systemic circulation, degradation of the C21 esters rapidly releases the inactive acid metabolites. Interestingly, the half-lives in guinea pig plasma for the two different ester stereoisomers were quite different, being 2.6 and 166.8 min for the 20R-dihydroprednisolonate and 20Sdihydroprednisolonate, respectively. Somewhat later, steroid-17-yl methyl glycolates with a succinyl group at C20 derived from prednisolone and dexamethasone (5-82) were investigated by the same group [379]. In fact, this is again a pro-soft drug type of approach, as first an active compound, the 21-ester, is released, and then this is metabolized further into an inactive metabolite. In a separate study, three series of compounds were synthesized in which sulfur-containing amino acids were incorporated into the steroidal structure at the 21-position [380]. The rationale for this, which the authors considered as being moreor-less along the principles of SD design, was that sulfur-containing compounds have shown, generally, a good cutaneous distribution as well as relatively rapid biotransformation and fast elimination, with the oxidized metabolites being inactive in most cases. However, drugs relying on such oxidative metabolism cannot be considered true SDs (see also the next paragraph). The compound in this series selected as most

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promising was 5-83, for which in vivo results showed a local activity about 10 times less and a systemic activity about 970 times less than that of dexamethasone. Finally, before closing this section, it should be mentioned that some other steroid drugs, such as fluticasone propionate (FP, 5-44), tipredane [381,382], and butixocort 21-propionate [383,384], are often and erroneously called soft drugs (see, e.g., [237,361,385]). These drugs are indeed metabolized rapidly, but by oxidative mechanisms in the liver [382,384,386]. Thiol ester corticosteroids such as FP have been shown to be metabolized in the liver by oxidative cleavage of the thiol ester bond and not by hydrolysis in the plasma [386]. Consequently, even if FP itself lacks oral activity because of high hepatic first-pass metabolism to the corresponding (inactive) 17-carboxylic acid, it has systemic effects if given subcutaneously [387]. In a few cases, even severe systemic side effects have been seen with inhaled or intranasal FP, especially when coadministered with CYP3A4 inhibitors [268,269,388,389], and since 2004, the combination of inhaled fluticasone and ritonavir is no longer recommended by GlaxoSmithKline, because of the risk of Cushing’s syndrome, unless the benefits overcome the risks [389]. FP was found to have a terminal half-life of about 8 h in 12 healthy male subjects after inhaled administration of 500, 1000, and 2000 ␮g of drug using a metered-dose inhaler. In these subjects, it produced dose-related cortisol suppression; the highest administered dose of FP resulted in cortisol concentrations that were lower than the limit of detection [390]. The slow elimination of FP led to accumulation during repeated dosing. This accumulation may explain the marked decrease in plasma cortisol seen during treatment with FP within the clinical dose range [391]. Furthermore, it is a highly lipophilic steroid and it shows increased terminal half-lives after inhalation, which usually is an indication of slow, rate-limiting absorption (“flip-flop pharmacokinetics”) [255]. In fact, FP has been shown to exhibit significantly steeper dose-related systemic bioactivity as measured by cortisol suppression than that of beclomethasone dipropionate (BDP; 5-48), budesonide (BUD; 5-41), or triamcinolone acetate [260]. Even if it is unrelated to the present topic (glucocorticoids), this is a good place to mention that by following a somewhat similar logic, a series of 4-(alkylthio)- and 4-(arylthio)benzonitrile derivatives prepared by Mitchell and co-workers at Pfizer were also incorrectly labeled by them as soft drugs [392]. These compounds were prepared as androgen receptor antagonists for the topical suppression of sebum production (intended as possible acne treatment), and they incorporated a thioether with the intention of providing metabolic lability via formation of the sulfoxide metabolite, which is inactive. However, again, this is an oxidative route and is unlikely to be a good choice for topical inactivation, not to mention that no metabolic data were provided in the paper [392] to support such metabolic inactivation. Ciclesonide (CIC, 5-47) [393,394] is sometimes also classified erroneously as a soft drug (see, e.g., [361,395,396]). As illustrated clearly in Figure 5-16, ciclesonide, just as beclomethasone dipropionate (5-48), is an inactive ester prodrug that has to be transformed metabolically into its active metabolite (CICam ) to exert activity at the GR. In fact, ciclesonide is slightly more lipophilic, but otherwise very similar to beclomethasone dipropionate. Both are inactive 21-esters activated by hydrolysis,

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INACTIVE METABOLITE–BASED SOFT DRUGS O OH

HO

OH

5-84

5-85

calcitriol (1 α ,25-dihydroxyvitamin D3)

maxacalcitol

OH

OH

HO

O O

24

R

OH

X O X: O, NH

16

5-87

5-86

calcitronic acid

HO

OH

HO

OH

FIGURE 5-36. Soft calcitriol (vitamin D3 ) analogs (5-86) as possible antipsoriatics with improved therapeutic index since they can be metabolized into structures resembling the biologically inactive calcitronic acid (5-85).

both have active metabolites that are 17␣ monoesters, and both have about similar binding affinity values (Figure 5-18) [279]. 5.3.4

Soft Calcitriol (1␣,25–Dihydroxyvitamin D3 ) Analogs

Calcitriol, a naturally occurring hormone (1␣,25-dihydroxyvitamin D3 ; 5-84, Figure 5-36), has antiproliferative and cell differentiation activities that could be of potential therapeutic use if sufficiently separable from the undesirable calcemic activity. Analogs such as maxacalcitol (5-85) have been developed along these lines for treatment of various diseases, such as psoriasis, secondary hyperparathyroidism, and osteoporosis, but they still require careful administration due to potential toxicity. Introduction of a 16-position double bond in such structures was explored by Shimizu’s group at Chugai Pharmaceutical in Japan to accelerate the oxidative metabolism in liver, presumed to be essential for the reduction of calcemic activity [397]. Later, in what qualifies as a true SD approach to develop antipsoriatics with improved TI, ester or amide moieties were inserted at the 24-position (5-86) to impart metabolic

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sensitivity, with the knowledge that calcitriol’s calcitronic acid metabolite (5-87) is biologically inactive [398]. This series (5-86) has been shown to have some extremely potent agonists of the vitamin D receptor with low calcemic action, and biological evaluations identified two structures as extremely potent low-calcemic vitamin D3 analogs, with concentration in rat skin comparable to that of maxacalcitol. These compounds [5-86; X = O, R = C(CH3 )(C2 H5 )2 and X = N, R = CH2 CF2 CF3 ] are being evaluated further for possible clinical application as antipsoriatic agents.

5.3.5

Soft Estrogens

The three major naturally occurring estrogens, the primary female sex hormone, are estradiol (5-88), estriol (5-89), and estrone (5-90) (Figure 5-37). They represent another important group of steroids in which soft drug approaches can provide new therapeutic agents with a beneficial separation of local and systemic effects. Menopause-related estrogen depletion is probably associated with a variety of symptoms, ranging from vasomotor complaints to cognitive deficits. Estrogen administration (hormone therapy) is known to alleviate most of these symptoms, but

FIGURE 5-37. The chemical structures of estrogens, the primary female sex hormone (including estradiol 5-88, estriol 5-89, and estrone 5-90), and of soft estrogens (5-91 to 5-94) designed for the treatment of vaginal dyspareunia.

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because of an association with increased risk for cancer, stroke, and other metabolic diseases, such therapies are either not recommended for, or are avoided by, many women. This is especially true now that large-scale parallel randomized double-blind placebo-controlled clinical trials undertaken within the Women’s Health Initiative to determine whether conjugated equine estrogen alone (in women with prior hysterectomy) or in combination with progestin (medroxyprogesterone acetate) reduces cardiovascular events in postmenopausal women had to be halted early because of unfavorable outcomes (increased risk for invasive breast cancer, stroke, and coronary heart disease) (see also Section 6.1.13 for a much more detailed discussion of these issues). Vaginal dyspareunia is a common disease affecting a large proportion of menopausal women (around 40% within 10 years of the onset of menopause), and topical application of estrogen has been used for treatment. Locally active soft estrogens with reduced systemic activity may provide a therapeutic alternative. Estradiol (E2 ; 5-88), the most potent human estrogen, provides a good starting point for an SD design. Along these lines, in research work carried out by Hochberg and co-workers at Yale University School of Medicine, a series of estradiol-16␣-carboxylic acid esters (5-91, Figure 5-37) were synthesized and examined first [399]. Whereas none of the acids (5-91, R = H, m = 0, 1, 2) showed significant estrogen receptor binding, the esters did. For them, receptor binding decreased with increasing m (Figure 5-37) or branching of the alcohol portion (R = isopropyl, neopentyl), but not with increasing length of the alcohol chain (R = methyl–butyl). The rate of hydrolysis in rat hepatic microsomes increased with increasing chain length (methyl to butyl) and was especially high for fluorinated alcohol chains (e.g., R = CH2 CHF2 ). Three of the most promising compounds (5-91, m = 0, R = CH3 , CH2 CH3 , CH2 CH2 F) were also tested for systemic and local action in rodent in vivo models. All of them, especially the fluoroethyl ester, showed good separation of the local and the systemic estrogenic action. In a follow-up study by the same group, additional carboxylic acid ester derivatives of estradiol have been synthesized with substitutions at the 7␣-, 11␤-, and 15␣positions (5-92–594) [400]. Whereas, again, all short-chain carboxylic acids were devoid of hormonal activity (as required by the inactive metabolite principle), some of the esters (e.g., formate esters, m = 0, at 7␣ and 15␣) showed good estrogenic activity. The position of the ester moiety relative to the steroid nucleus seemed important, as lengthening of the chain from formate to acetate dramatically decreased hormonal activity. In general, lengthening of the alcohol moiety (from methyl to butyl) had only a small effect on receptor binding, but tended to increase esterase activity. The more promising candidates were also tested in vivo for local and systemic estrogen activity by using vaginal assay in mice and uterotrophic assay in rats, respectively, and the methyl and ethyl esters of estradiol 15␣-formate (5-92, m = 0, n = 0, 1) showed high local and low systemic activity; hence, they are considered as the most promising soft estrogens and are being developed for clinical applications. Strangely, it turned out that among the 11-substituted compounds (5-94), increasing the ester substituent with a single methylene unit converted the compound from an estrogen to an antiestrogen. Whereas the methyl ester (5-94, m = 1, n = 0) has high ER affinity

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HO

HO

NH

NH

5-96

5-95

procaterol

terbutaline HO

O

OH

NH OH

soft drug design O HO

HO

R

NH n

NH

O

5-97

5-98

O NH

R

HO OH

O

OH

FIGURE 5-38. Soft ␤2 -agonists (5-97, 5-98) designed on the basis of lead structures such as terbutaline (5-95) or procaterol (5-96).

and high estrogenic potency, the ethyl ester (5-94, m = 1, n = 1) had even higher ER affinity but little or no estrogenic activity [401,402]. Thus, this small modification resulted in an unusual, steroidal selective estrogen receptor modulator.

5.3.6

Soft ␤2 -Agonists

Terbutaline and Procaterol Analogs Beta2 -agonists represent an important class of drugs in the therapy of asthma because of their ␤2 -receptor-mediated bronchodilating activity [403]. Compounds such as terbutaline (5-95, Figure 5-38), fenoterol, and salbutamol are chemical analogs of epinephrine (adrenaline), the prototypical adrenergic agonist, but they are ␤2 -selective agents. These agents, including the longer-acting formoterol or salmeterol (5-99), are most frequently taken by aerosol. The majority of the drug administered this way is swallowed, and only about 10 to 25% of the dose reaches the lung directly. Therefore, there is a great potential to produce unwanted side effects, such as tachycardia or skeletal muscle tremor. Again, an SD approach might yield viable solutions. Incorporation of a metabolically labile ester group into such structures has been attempted both in the nitrogen substituent [404] and on the aryl system [405] (Figure 5-38) by Albrecht and co-workers at Schering. The activity of compounds 5-97 (n = 0 or 1, R = CH3 ) and 5-98 (R = CH3 ) surpassed that of terbutaline (5-95) or isoprenaline. The corresponding carboxylic acids (5-97 or 5-98, R = H) are essentially devoid of ␤2 -agonist activity (they are one to four orders of magnitude less potent); therefore, their use as inactive metabolites in the design of more potent esters is justified (Figure 5-38) [404,405]. Compound 5-98 with R = CH3 (ZK 90.055) was selected for further pharmacological and toxicological evaluation. Consistent with the design, it rapidly hydrolyzed in the presence of guinea pig liver homogenate and showed good in vivo bronchospasmolytic activity when

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given by inhalation. Meanwhile, it was almost inactive on oral administration and had no effect on the heart rate of guinea pigs following inhalation of up to 10-fold the dose that was active in the bronchospasm experiment [405]. Soft ␤-agonist structures were also investigated as possible soft antipsoriatic agents [406]. Both soft drugs and pro-soft drugs were synthesized as models of topical antipsoriatic ␤-adrenergic agonists. The structure of the SDs was similar to that of 5-97 (Figure 5-38, n = 1, R = CH3 , CH2 CH3 ), and the pro-SDs were obtained by esterification of the phenolic functions. In the presence of porcine liver carboxyesterase, the pivaloyl ester groups of the prodrug underwent rapid hydrolysis (t1/2 = 8.4 min) to release the soft drug, which then also underwent hydrolysis (t1/2 = 456 min) to the inactive carboxylate anion [406]. The SD was a full ␤-agonist on the guinea pig tracheal preparation, producing a maximal response similar to that achieved with isoprenaline. The pro-SD produced only slowly developing responses at high concentrations (>10 ␮M) and had better transport properties across a silicone membrane. Soft Salmeterol Analogs: Vilanterol In a more recent approach by Procopiou and co-workers from the same group at GlaxoSmithKline that explored the series of glucocorticoid ␥ -lactones discussed earlier here as possible soft steroids, soft analogs of the long-acting ␤2 -agonist salmeterol (5-99, Figure 5-39) have been synthesized by insertion of one additional oxygen atom into the ring-connecting long chain to increase the metabolic lability (5-100; Figure 5-39). Strictly speaking, this approach should be classified under the soft analog class that will be discussed later, but for

FIGURE 5-39. Vilanterol (5-101) as a metabolically labile analog of the long-acting ␤2 agonist salmeterol (5-99). Vilanterol does not contain a hydrolytically labile ester, but benzylic hydroxylation, probably the main route of metabolism, results in an unstable hemiacetal (5-102) that cleaves into the corresponding alcohol (5-103), which is much less active.

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convenience, it is discussed briefly here [407]. It is also a nonconventional SD approach as defined here, as it does not involve a hydrolytic inactivation. In a first attempt, replacement of the phenyl ring of salmeterol-type ␤2 agonists with a ␥ -lactone moiety was attempted (to rely on the same paraoxonase-mediated hydrolysis that was already employed by this research group); however, they were found to have low efficacy. As a next possibility, insertion of one oxygen in the salmeterol chain was explored with the assumption that benzylic hydroxylation remains the main route of metabolism, and the resulting metabolite will be an unstable hemiacetal (e.g., 5-102) that cleaves into the corresponding alcohol (5-103), which is probably much less active, and benzaldehyde, which will quickly be further oxidized into nontoxic moieties. The resulting most promising compound obtained on the basis of this rationale, vilanterol (5-101), is a long-acting ␤2 -adrenoceptor agonist that is currently in clinical trials as Relovair, a once-daily inhaled corticosteroid/long-acting ␤-agonist combo (fluticasone furoate/vilanterol). Interestingly, not only is vilanterol more potent than salmeterol with a faster onset of action, but in human tissue, it also has a significantly longer duration of action: vilanterol still demonstrated a significant bronchodilator effect 22 h after administration, but salmeterol did not ([408] and references therein). In the meantime, consistent with its design, vilanterol is metabolically labile and in human microsomes undergoes conversion to metabolites with significantly lower ␤2 -adrenoceptor activity, exhibiting low systemic exposure in vivo after inhaled dosing [408]. Various promising clinical results have already been obtained [408]. Vilanterol (5-101) has been tested in both asthmatic and chronic obstructive pulmonary disease (COPD) patients. In mild to moderately persistent asthma patients, single doses of inhaled vilanterol (25 to 100 ␮g) produced rapid and prolonged bronchodilation over 24 h, suggesting the potential for once-daily administration. All doses were well tolerated, with no clinically significant unwanted systemic effects. Vilanterol (25 to 100 ␮g) produced rapid bronchodilation in COPD patients which was maintained over 24 h at all doses. Vilanterol was absorbed rapidly into plasma (median tmax of 10 min) with systemic exposure increasing in an approximately dose-proportional manner across the vilanterol dose range. A 28-day study in patients more than 12 years old with persistent asthma on maintenance inhaled corticosteroids showed that once-daily vilanterol was well tolerated and resulted in a prolonged duration of action of at least 24 h at doses at or above 12.5 mg. Also, a 28-day dose-ranging (3, 6.25, 12.5, 25, or 50 ␮g) study in patients with COPD demonstrated statistically significant improvements in trough forced expiratory volume in 1 s for all doses, compared with placebo (p < 0.001). 5.3.7

Soft Psychostimulants

Methylphenidate Methylphenidate (5-104, Figure 5-40), a methyl ester–containing piperidine derivative that is structurally related to amphetamine (5-106, Figure 5-40), is a mild central nervous system (CNS) stimulant, with more prominent effects on mental than on motor activities, that has been in clinical use for almost 50 years. More recently, it has become widely used as the most prescribed drug in child and adolescent psychiatry (e.g., Ritalin, Concerta, Daytrana) for the treatment of

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H N

H N

O

143

OH

O

O

5-104

5-105

methylphenidate

ritalinic acid (inactive)

OH OH

H N

(R)

O

H 2N

H 2N (S)

(R)

O 5-104 (R, R) (d)-R,R-methylphenidate (most active isomer)

5-106

5-107

(d)-S-amphetamine (most active isomer)

dopamine

FIGURE 5-40. Methylphenidate (5-104) is an example of a metabolically degradable soft psychostimulant, as it is cleaved rapidly and hydrolytically into the inactive ritalinic acid (5-105). The structures of amphetamine (5-106) and dopamine (5-107) are shown for comparison together with that of the (R,R) isomer of methylphenidate, which is the most active isomer.

attention-deficit-hyperactivity disorder (ADHD). Its mechanism of action is not entirely elucidated, but there is mounting evidence for a dopaminergic basis of its action. Its therapeutic effects in ADHD treatment seem to be elicited primarily through inhibition of the presynaptic dopamine transporter [409,410]. Methylphenidate (5-104) has two chiral centers and therefore a total of four isomers: erythro-RS-d–, erythro-SR-l–, threo-SS-l–, and threo-RR-d–methylphenidate (Figure 5-40). Early formulations contained all four isomers, but later the erythro isomers have been removed because of their association with some adverse effects. Most of the effect seems to reside with the d-enantiomer, and an enantiopure formulation (Focalin) has recently been introduced on the market, but most marketed formulations are 50 : 50 mixtures of the threo-SS-l– and the threo-RR-d–methylphenidate isomers. Methlyphenidate is rapidly hydrolyzed [411] into an inactive [412] acidic metabolite (ritalinic acid; 5-105); therefore, it can be considered a soft drug, even if it was not designed as such. This is certainly the main reason behind its safety, which makes possible its widespread pediatric use. Nevertheless, methylphenidate is a schedule II drug, just as are amphetamines (i.e., it is considered a medication of high abuse potential). Plasma concentrations of the ritalinic acid metabolite 5-105 are much higher than those of the parent 5-104 (e.g., mean AUC values were 23 ± 4 times greater [411]), and 60 to 80% of methylphenidate is eliminated in urine as its acidic metabolite [410]. The hydrolysis of 5-104 is enantioselective, with the active d isomer being apparently less susceptible to hydrolytic degradation [413]; consequently, the d-enantiomer has a longer half-life and is present in higher concentrations. Methylphenidate is

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distributed rapidly (tmax around 2 h after oral administration), and because of its fast, mainly hydrolytic metabolism, it is also cleared rapidly; its elimination half-life is around 2 to 3 h, which allows no day-to-day accumulation [409,410]. In fact, its short half-life is one of its main problems, because immediate-release formulations require relatively frequent administration to maintain effectiveness, an inconvenience especially in pediatric populations. Extended-release or transdermal formulations seem to provide good solutions, and they also make possible a sort of ideal SD therapeutic approach: slow release or infusion to maintain a safe pharmacological effect that disappears rapidly when administration stops. Because with methylphenidate the greatest behavioral effect in ADHD seems to occur when there is a rising blood concentration (called a gradient effect), most newer extended-release formulations try to achieve not a concentration plateau, but prolonged or multiple rising phases [410]. 5.3.8

Soft Insecticides and Pesticides

Notably, the same concepts as those used for soft drug design can be extended to the design of less toxic commercial chemical substances, provided that adequate structure–activity relationship and structure–metabolism relationship data of analogous substances can be gathered (soft chemical design). The following two examples are not actual designs based on such principles, but observations made in hindsight. Nevertheless, they illustrate the possibilities inherent in such approaches. Also, they provide examples for the design of environmentally safe, nontoxic chemicals (green chemistry) [414]. Chlorobenzilate One instance in which these principles have been used (unintentionally) for the design of nonpharmaceutical products is chlorobenzilate (Acaraben, Folbex 5-115), an ethyl ester–containing analog of dicofol (5-109) and dichlorodiphenyltrichloroethane (DDT, chlorophenothane; 5-108, Figure 5-41). DDT was the first chemical that revolutionized pest control, and it was also used to control typhus and malaria. Although synthesized in 1874 by Zeidler, its insecticidal properties were not discovered until 1939 by M¨uller [415,416]. It was widely used by the German army and later by the U.S. army during World War II. It became used extensively as a pesticide in the United States, but it was banned in 1972 for all but essential public health use and a few minor uses. The decision was prompted by the prospect of ecological imbalance from continued use of DDT, by the development of resistant strains of insects, and by suspicions that it causes a variety of health problems, including cancer. DDT undergoes complex in vivo metabolism, including oxidation (5-109, 5-110), iterative dehydrohalogenation/reduction cycles (5-111– 5-113), and hydrolysis (5-114) (Figure 5-41) [417]. The acid metabolite 5-114 (DDA) is of low toxicity, can be excreted as a water-soluble species, and is, indeed, a major metabolite detected in feces and urine. Therefore, it is an ideal lead compound for a formal inactive metabolite approach (Figure 5-41). Not surprisingly, the corresponding ethyl ester, ethyl-4,4 -dichlorobenzilate (chlorobenzilate, 5-115), is also active as a pesticide but has much lower carcinogenicity than DDT (5-108) or dicofol (kelthane, 5-109). For example, the carcinogen

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H Cl

Cl

H

CCl 3

Cl

Cl CHCl2

5-108

5-112

DDT

DDD Cl

Cl

Cl

Cl

CCl2

CCl 3

Cl

Cl

5-111

5-109

CH 2

DDE 5-113

dicofol (kelthane)

"DDNU"

H Cl

Cl

Cl

Cl O

5-110

OH

17:32

O

5-114

dichlorobenzophenone

DDA

OH

OH Cl

O 5-115 chlorobenzilate

O

Cl

Cl O

OH

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FIGURE 5-41 Chlorobenzilate (5-115) can be regarded as a soft chemical obtained using an inactive metabolite–based approach starting from the metabolism of DDT (5-108), one of the best-known synthetic insecticides.

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FIGURE 5-42. Malathion (5-119), its oxidative activation to malaoxon (5-120), a much more active cholinesterase inhibitor, and its deactivation by carboxylesterases. Oxidative activation is the dominant pathway in insects, but hydrolytic deactivation is the dominant pathway in mammals. Because of its hydrolytic deactivation in mammals, malathion (5-119) is much less toxic than other organophosphates, such as phorate (5-117) or parathion (5-118), as illustrated by the corresponding LD50 values, which are also included in the figure.

concentration determined in mice is 6000 mg/kg for chlorobenzilate compared to 10 and 264 mg/kg for DDT and dicofol, respectively [418,419]. Similarly, the oral median lethal dose (LD50 ) for female rats is 1220 mg/kg for chlorobenzilate compared to 118 and 1000 mg/kg for DDT and dicofol, respectively [420]. The ethyl ester moiety apparently can replace the trichloromethyl group of DDT to restoring pesticidal activity. However, because in exposed subjects, the labile ethyl ester group enables rapid metabolism to the free, nontoxic carboxylic acid 5-116, chlorobenzilate is considerably less toxic than DDT. Malathion Malathion (Derbac-M, Malaspray; 5-119, Figure 5-42) is an excellent example to illustrate an additional, not yet sufficiently explored aspect of the design of soft chemicals. As mentioned, it is desirable to design soft chemicals deactivated by carboxylesterases. For soft chemicals intended to be used as pesticides, in addition to the usual advantages of SD design, the differential distribution of these enzymes between vertebrates and insects may also provide selectivity based on metabolism. An elegant example is provided by malathion (5-119), a widely used organophosphate insecticide (Figure 5-42). Malathion is detoxified through a variety of metabolic

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pathways, one of the most prominent being the hydrolysis of one of its two ethyl carboxylester groups leading to 5-121. The carboxylesterase that hydrolyzes and thereby detoxifies malathion is widely distributed in mammals but only sporadically in insects, where in some rare cases it is responsible for insecticide resistance [421,422]. In the meantime, insects seem to possess a very active oxidative enzyme system that transforms malathion (5-119) into malaoxon (5-120), a much more active cholinesterase inhibitor (Figure 5-42). Probably, all insects and all vertebrates possess both an esterase and an NADPH-dependent oxidase system, but the balance of action of these two systems varies from one organism to another and provides selectivity of action. A similar mechanism may provide considerable selectivity for other soft chemicals to be designed and may result in safer, soft insecticides, for example, in the parathion family. These compounds are not susceptible to such deactivation mechanisms and, consequently, have unacceptably high mammalian toxicities. For example, acute oral LD50 values in male rats are 2 and 5 mg/kg for phorate (5-117) and parathion (5-118), respectively, compared to 1400 mg/kg for malathion (5-119). 5.3.9

Soft Anticholinergics: Inactive Metabolite–Based Approach

Soft anticholinergics provide a good illustration of the flexibility and potential of the general soft drug design concept. Our work in this area resulted in two entirely different classes of soft anticholinergics. Inactive metabolite–based classes (5-129, 5-131, Figure 5-43), which are discussed here, were obtained by using methylatropine (5-123) [423–428], N-methylscopolamine (5-124) [429,430], or glycopyrrolate (5-126) [431] as lead. The soft analog class (5-133), discussed in the following section, contains soft quaternary analogs [170,432]. Muscarinic receptor antagonists inhibit the effects of acetylcholine by blocking its binding to muscarinic cholinergic receptors at neuroeffector sites on smooth muscle, cardiac muscle, and gland cells, as well as in peripherial ganglia and in the CNS; therefore, they are used or are of therapeutic interest for a variety of applications, including treatment of asthma and COPD, prevention of motion sickness, mydriasis and cycloplegia, Alzheimer’s and Parkinson’s disease, and disorders of intestinal motility, cardiac function, and urinary bladder function [433,434]. Muscarinic antagonists include the naturally occurring alkaloids of belladonna plants such as atropine and scopolamine. Quaternary derivatives are usually more active, and they cannot cross the blood–brain barrier and reach the CNS (usually an advantage, because they are less likely to cause CNS-related side effects), but they also tend to be more poorly absorbed, which might cause problems in ensuring adequate bioavailability. Obviously, subtype selectivity, if achievable, can provide increased therapeutic advantage [435]. Because of their ability to inhibit local antisecretory activity, anticholinergics have even been explored as antiperspirants for excessive sweating [436–440]. There is renewed interest in these agents, due to their applicability as inhaled bronchodilators for the treatment of bronchospasm associated with COPD and other diseases [tiotropium (5-125), ipratropium] [441]. However, in many cases, their use is still limited by the possibility of a number of side effects, such as cardiac arrhythmias, tachycardia, dry mouth, difficulty in urination, constipation, photophobia, irritability,

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FIGURE 5-43. Soft anticholinergics: design and metabolism of compounds obtained based on the inactive metabolite–based approach (5-129, 5-131; substitutions at two different positions) and the soft analog approach (5-133) together with a set of representative classic (5-123 to 5-127) and soft (5-129a–d, 5-131a–d, 5-133a–d) quaternary anticholinergic structures. All soft structures can be traced back to a common general lead (5-128), and they are all inactivated in a single hydrolytic step. pA2 values shown are for in vitro anticholinergic activity determined by guinea pig ileum assay with carbachol as agonist.

restlessness, disorientation, dementia, and hallucinations [434,442]. Even topically applied anticholinergics can cause unwanted side effects [443–451] because they are absorbed into the systemic circulatory system and are eliminated only relatively slowly. A locally active soft drug may again represent a workable solution. To obtain SD compounds that have high local but practically no systemic activity, a series of soft anticholinergics based on methylatropine, N-methylscopolamine, glycopyrrolate, or propantheline were designed in our laboratories [452,453].

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Several new molecules synthesized were found to be potent anticholinergics both in vitro and in vivo, but in contrast to their hard analogs, they had no systemic anticholinergic activity following topical administration. For example, methylatropineor methylscopolamine-derived phenylmalonic acids (5-129, n = 0) [423–426,428, 429] and phenylsuccinic acids (5-129, n = 1) [427,430] served as useful hypothetical inactive metabolites for the design of soft anticholinergics. This inactive metabolitebased approach exploits the idea that the benzylic hydroxy function in methylatropine (5-123) or methylscopolamine (5-124) could be oxidized to the carboxylic acids 5-130 (n = 0), which are hypothetical [442,454,455] inactive metabolites. Esterification of this carboxy function to afford soft drug series 5-129 may restore activity while ensuring facile, hydrolytic deactivation. Soft anticholinergic esters of this type (5-129) showed good intrinsic activity, as indicated by the pA2 value of 7.85 of tematropium (PMTR.Et, 5-129a) compared with 8.29 for atropine. However, in vivo activities were much shorter than for the “hard” atropine. Accordingly, when equipotent mydriatic concentrations of atropine and tematropium (5-129a) were compared following ocular administration, the same maximal mydriasis was obtained, but the area under the curve (mydriasis vs. time) for the soft compound was only 11 to 19% of that for atropine [423,425]. This is consistent with the facile hydrolytic deactivation of the soft drug. Similarly, the cardiovascular activity of compound 5-129a showed ultrashort duration. The effect of 5-129a on the heart rate and its ability to antagonize the cholinergic cardiac depressant action induced by acetylcholine injection or by electrical vagus stimulation was determined in comparison with atropine (sulfate) and methylatropine (nitrate). A dose of 1 mg/kg of atropine or methylatropine could completely abolish the bradycardia induced by acetylcholine injection or by electrical vagus stimulation for more than 2 h following i.v. injection. On the other hand, similar doses of tematropium exerted antimuscarinic activity for only 1 to 3 min following i.v. injection. Even a 10-fold increase in its dose, to 10 mg/kg, did not lead to any significant prolongation of the duration of anticholinergic activity. As a further variation, the corresponding ester analogs of methylscopolamine were also investigated [429]. Again, the in vivo anticholinergic activity was examined in rabbit eyes after unilateral administration into one eye, with the untreated eye serving as a control to measure the systemic activity of the compounds. Several soft drugs were shown to be potent mydriatic agents in rabbits, and the optimal anticholinergic activity was achieved with a two- or three-carbon alcohol chain. At equieffective doses, the mydriatic recovery period, the time taken for pupil dilation to reach within 0.5 mm of the baseline, was 20 h for methscopolamine compared to 3.7 h for PMSC.Et and 3 h for PSSC.Et (5-129b) (Figure 5-44). The areas under the mydriatic response vs. time curve for the treated eye were only 22% and 21.5% of that of methscopolamine for PMSC.Et and PSSC.Et, respectively. In the same experiment, tropicamide was longer acting than both PMSC.Et and PSSC.Et. Furthermore, consistent with the SD approach, the untreated eye showed no mydriasis after administration of the soft drugs, only after the administration of methscopolamine (and some marginal effect after administration of tropicamide) (Figure 5-44). Other structures, such as the cyclopentyl derivative 5-129c (PcPMTR.Me, PCMS-2) [428] or different

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Treated eye

2.0

Pupil dilaon (mm)

N-Me Scopolamine

1.5

PMSC.Et PMSC.Pr PMSC.Bu

1.0

PSSC.Et PSSC.Pr Tropicamide

0.5

Control

0.0 0

2

4

6

8

10

12

-0.5 Time (h) 1.5

Untreated eye

1.3 1.1 Pupil dilaon (mm)

N-Me Scopolamine

0.9

PMSC.Et PMSC.Pr

0.7

PMSC.Bu PSSC.Et

0.5

PSSC.Pr Tropicamide

0.3

Control

0.1 -0.1 0

1

2

3

4

5

6

-0.3 Time (h)

FIGURE 5-44. Mydriatic activity of N-methylscopolamine analog soft anticholinergics in rabbits following unilateral administration of equieffective doses. Due to their metabolic inactivation, soft drugs are shorter acting then methylscopolamine in the treated eye (top figure) and do not produce mydriatic effect in the untreated, contralateral eye (bottom figure).

phenylsuccinic analogs of methylatropine [427] and methylscopolamine [430], were also investigated. Compound 5-129c (PcPMTR.Me or PCMS-2) was equipotent to atropine in protecting against carbachol-induced bradycardia in rats, but its duration of action was again significantly shorter (15 to 30 min vs. more than 2 h) (Figure 5-45) [428]. Similar ester analogs (PcPMGP.Me, 5-129d) of glycopyrrolate (5-126) were also explored [431]. For soft anticholinergics, the inactive metabolite-based approach can also yield a different class of compounds in which the hydrolytically labile ester-containing side chain is attached to the quaternary nitrogen head (5-131). A number of such compounds derived from methylatropine (e.g., PcPATR_NA.Me 5-131a, 5-1314b) [456,457] or glycopyrrolate (e.g., PcPOAGP_NA.Et 5-131c) [458–463] have recently

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SOFT ANALOGS 20

*

10

*

*

*

*

% Change in heart rate

0 -10

0

10

20

30

40

50

60

-10

Vehicle Atropine (0.2 μmol/kg) PCMS-2 (0.2 μmol/kg) PCMS-2 (2.0 μmol/kg)

-20 -30 -40 -50 -60 -70 -80

*

* Time (min)

FIGURE 5-45. Protective effect of the soft anticholinergic 5-129c (PcPMTR.Me or PCMS-2) against carbachol-induced bradycardia [428]. Each point represents the mean ± SD of three experiments. Significant differences (p < 0.05) from both 0.2 and 2.0 ␮mol/kg PCMS-2 are denoted with by an asterisk; the vehicle is 0.9% NaCl. The potency of action of the soft drug is similar to that of atropine and the other traditional anticholinergics, as it is effective at the same dose range; however, its action lasts only up to 15 to 30 min, again emphasizing its soft nature.

been synthesized and tested. Several of them showed promising and stereospecific in vitro receptor binding and in vivo mydriatic activities as well as some degree of and M3 /M2 muscarinic-receptor subtype selectivity. Furthermore, compounds such as 5-131d (PcHCTR_NA.Me) have also been explored in an attempt to obtain soft anticholinergics with muscarinic receptor subtype selectivity [464]. Hence, a lead compound (LG50643) was selected that has been shown to be a potent and selective antagonist for the M3 receptor subtype [465], and it was derivatized following the same procedure to obtain soft compounds such as 5-131d. Receptor-binding studies on cloned muscarinic receptors indicated that these soft anticholinergics have reasonable activity (pKi values of 7.5 to 8.9) and that two of them show muscarinic receptor subtype selectivity (M3 /M2 ) [464]. Consistent with their soft nature, these compounds were short acting and were rapidly eliminated from plasma.

5.4

SOFT ANALOGS

Soft drugs classified as soft analogs are close structural analogs of known active drugs (lead compounds), but they have a moiety that is susceptible to metabolic,

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preferentially hydrolytic, degradation built into their structure. The built-in metabolism should be the major, and preferentially, the only metabolic route for drug deactivation (predictable metabolism), and the rate of the predictable metabolism should be controllable by structural modifications (controlled metabolism). The predicted metabolism should not require enzymatic processes leading to highly reactive intermediates, and the products resulting from the metabolism should be nontoxic and have no significant biological or other activities (metabolic inactivation). Finally, the metabolically weak spot should be located within the molecule so that the overall physical, physicochemical, steric, and complementary properties of the soft analog are very close to those of the lead compound (isosteric/isoelectronic analogy). 5.4.1

Soft Anticholinergics: Soft Quaternary Analogs

As mentioned, in addition to the inactive metabolite–based classes of soft anticholinergics, an entirely different class containing soft quaternary analogs has also been explored. Structural differences between “hard” and “soft” anticholinergics of this type are relatively small but nonetheless profound, as illustrated on the right side of Figure 5-43. Soft analog anticholinergic structures were obtained by shortening the bridge of two or three carbon atoms separating the quaternary head and ester function of traditional hard anticholinergics illustrated by the generalized structure 5-128 (k = 3) to just one carbon separation, as shown in structure 5-133. This allows facile hydrolytic deactivation via a short lived intermediate to the corresponding acid (5-134), tertiary amine, and aldehyde, all inactive as anticholinergics, as shown in Figure 5-43. At the time of the design, a separation of at least two carbon atoms between the ester oxygen and the quaternary nitrogen of such anticholinergic structures was thought to be critical for effective receptor binding. Nevertheless, several compounds of type 5-133 were found to be at least as potent as atropine [170]. For example, 5-133a (SQA.PcP-DMP) was equipotent with atropine in various anticholinergic tests, but it was very short acting after i.v. injection. Therefore, when applied topically to humans, it produced high local antisecretory activity but no systemic toxicity. In a more recent study, similarly designed soft analogs using propantheline (5-127) as lead have also been investigated as potential antiperspirants and antiulcerative agent [432]. Recently, we were able to put together a retrospective, comprehensive quantitative structure–activity relationship (QSAR) study for all quaternary soft anticholinergics (n = 76) discussed here from these two distinctly different classes, which were designed on the basis of the soft analog and the inactive metabolite approaches, respectively [453]. Modeled activity data included pA2 values (the negative logarithm of the molar concentration of the antagonist that produces a twofold right shift in the concentration–response curve of the agonist) measured using the in vitro guinea pig ileum assay and receptor binding pKi values measured with cloned muscarinic receptors (M3 subtype). Because of the clear biphasic nature of the activity data when all structures were considered as a function of molecular size (volume), a

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FIGURE 5-46. Comprehensive quantitative structure–activity relationship results [453] for the pA2 data of all quaternary soft anticholinergics (n = 76) using molecular volume V as a main descriptor and the bilinear LinBiExp model with additional structural descriptors [I_acid for the presence of a carboxylic acid (− −COOH), I_2R for enantiomerically pure 2R isomers, I_PS for succinic analogs where the carboxylic ester is one position away from the substitution −COOR in the malonic series), and I_cPe for the presence center (R3 = –CH 2 COOR vs. R3 = − of a cyclopentyl substitution at the 2-position (as in glycopyrrolate)].

nonlinear model had to be used (Figure 5-46), and the bilinear LinBiExp model [100,280] discussed before [eq. (5.10)] proved very adequate again [453]. This time, the equation was used in the form n
  pA2 = ␩ln e␣1 (V −␯c )/␩ + e␣2 (V −␷ c )/␩ + ␹ + ␦␸i I␸i

(5.12)

i=1

This QSAR study indicated that for quaternary anticholinergics, just as before for glucocorticoids, molecular size is again a major determinant of activity, and best activity is achieved with ligands not considerably smaller or larger than the known highly active anticholinergics, such as 5-123–5-127 (including methylatropine, N-methylscopolamine, and glycopyrrolate), which seem to be close to the ideal ligand size at these receptors. In agreement with SD design principles, acid metabolites were indeed essentially inactive: their activities being around two orders of magnitude less than those of the corresponding esters (␦acid = –1.942; actual experimental

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confirmation has been obtained for a total of five different acidic metabolites, as indicated in Figure 5-46: PMTR.H, PMSC.H, PSTR.H, PcPOAGP_NA.H, and 2RPcPOAGP_NA.H with the notation of Figure 5-43 and reference [453]). The importance of stereospecificity at muscarinic receptors was also confirmed on this series of compounds: 2R analogs are considerably more active than the corresponding isomeric mixtures. The effect of other substitutions (␸), such as inclusion of a scopolaminetype oxygen or a cycloalkyl ring, endo/exo isomerism, or distancing of the metabolically labile ester from the substitution center (phenylsuccinate vs. phenylmalonate esters), could also be quantified through the corresponding ␦␸ terms (Figure 5-46) [453]. 5.4.2

Soft Antimicrobials

Cetylpyridinium Analogs: Soft Quaternary Salts The very first soft analogs designed were “soft quaternary salts,” represented by the generalized structure 5-138 (Figure 5-47) intended for antimicrobial use [1,466]. Similar to the anticholinergic structures described previously, these substances undergo a facile hydrolytic cleavage process via a very short-lived intermediate (5-140) to deactivate and form an acid (5-141), an amine (5-142), and an aldehyde (5-143), as shown in Figure 5-47. As mentioned briefly in Section 5.3.1, this mechanism was initially designed to develop a prodrug of aspirin, the synthesis of which, however, was unsuccessful [467].

N Cl -

5-135 cetylpyridinium chloride

soft analog approach

O O

N Cl -

5-136 O O

N

N

Cl 5-137

R1

O R

O

N 5-138

R1

O

hydrolysis

+

+

R

OH 5-139

HO

O N

+ N

+

5-140

R

+ R 1CHO

OH 5-141

5-142

5-143

FIGURE 5-47. Cetylpyridinium chloride (5-135) and soft analog antimicrobials (5-136, 5137). The general hydrolytic deactivation mechanism of soft quaternary salts (5-138) via a very short-lived intermediate (5-140) to an acid (5-141), an amine (5-142), and an aldehyde (5-143) is also shown.

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The simplest example of useful true soft analogs (Figure 5-47) is provided by the isosteric analogs (5-136, 5-137) of cetylpyridinium chloride (5-135). Cetylpyridinium, a known hard quaternary antimicrobial agent, needs several oxidative (generally, ␤-oxidation) steps to lose its surface-active antimicrobial properties. The quaternary salts represented by 5-135 and 5-136 are very similar: Both contain side chains that are essentially 16 atoms in length. Their physicochemical properties are also very similar. For example, their critical micelle concentrations (CMCs) determined by a molecular light-scattering method are 1.3 × 10−4 M and 1.7 × 10−4 M, respectively [168]. Hard and soft compounds possess comparable antimicrobial activity as measured by their contact germicidal efficiency, but soft compounds undergo facile hydrolytic cleavage, leading to their deactivation. Because of this, the soft 5-137 is about 40 times less toxic than the hard 5-135; the corresponding oral LD50 values for white Swiss male mice are 4110 and 108 mg/kg, respectively. Environmentally Friendly Quaternary Analogs A set of similar cetylpyridinium chloride or benzalkonium chloride analogs have been synthesized and investigated, and the corresponding structure–activity relationships have been explored in additional detail more recently by Loftsson and co-workers at the University of Iceland [468–470]. Similar to 5-136 or 5-137, these soft antimicrobials also consisted of long alkyl chains connected to polar quaternary ammonium headgroups (pyridinium or trialkyl ammonium) via chemically labile spacer groups, but more flexibility was allowed (i.e., various polar quaternary headgroups and spacer distances were explored). Building blocks were selected preferentially from natural compounds, partly based on marine lipids, such as fatty acids, alcohols, or amides. The more active compounds had minimum inhibitory concentration as low as 1 ␮g/mL and viral reduction greater than 6.7 log units. Best activities were achieved with alkyl chain length of 12 to 18 carbon atoms, an observation long known for such antimicrobial compounds [471,472], and also with smaller quaternary heads and inactivation half-lives larger than 3 h at 60◦ C [469]. Before closing this chapter, we should mention that a set of ester-containing soft bisquaternary ammonium salts with two long aliphatic chains derived from bis(2-dimethylaminoethyl)glutarate have also been explored by a different group at Comenius University, Slovakia, and several of them were found to have adequate activity [473]. Long-Chain Esters of Betaine and Choline Another, unrelated effort initiated at G¨oteborg University, Sweden was directed toward the development of long-chain esters (5-144, 5-147) of betaine (5-149) or choline (5-146) as soft antimicrobial agents (Figure 5-48) [474–478]. These compounds are ester-containing structural analogs of amphiphilic quaternary ammonium compounds, which, similar to cetylpyridinium (5-135), are surface-active substances known for their membrane-disruptive and antimicrobial activities. Contrary to the design of cetylpyridinium analogs 5-138, however, hydrolysis here is not followed by additional, fast degradation but results in well-investigated and common compounds, such as choline (5-146), betaine (5-149), and fatty acids (5-145) (Figure 5-48). For example, the alkanoylcholines were found active against gram-negative and gram-positive bacteria, as well as yeasts [475].

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O

O

hydrolysis

+

N R

(enzymatic)

O 5-144 R = Cn H2n+1 ; n = 9-16

R

N HO

OH 5-145

5-146

fatty acid

choline

O

O

hydrolysis R

N

+

ROH

O

N

(spontaneous)

5-147

HO 5-148

5-149 betaine

O R1 O R2

O

O

hydrolysis

OH

N O

N HO

5-150 R1, R2 = Cn H 2n +1; n = 6-16

5-151 L-carnitine

FIGURE 5-48. Soft quaternary ammonium antimicrobials designed as a long-chain ester of choline (5-146), betaine (5-149), or l-carnitine (5-151).

Activity increased with increasing chain length and was similar to that of the stable, hard quaternary ammonium compounds of similar length, such as hexadecyltrimethylammonium bromide. Considerable differences in the binding affinity of compounds with different hydrocarbon chains at different concentrations to Candida albicans were observed, and they seemed related to the CMC of the compounds [476]. L-Carnitine Esters

In work carried out at Sigma-Tau and the University of L’Aquila, Italy, another class of soft broad-spectrum antimicrobials devoted to curing dermatological infections was designed based on quaternary ammonium l-carnitine esters (5-150) (Figure 5-48) [479]. The series, particularly members characterized by alkyl chains with a total of 16 to 18 carbons, showed good activity against a wide range of bacteria, yeasts, and fungi. They also showed low in vitro cytotoxicity and good in vivo dermal tolerance. However, the decomposition of these compounds was not analyzed in detail. Judicious esterification (R2 ) of the carboxy function is required for antimicrobial activity, but a free hydroxy group at position 3 of the carnitine skeleton does not annihilate activity. From the SD design point of view, it is also important to note that the common constituent of all these compounds, l-carnitine (5-151), has no pharmacological effects for doses of up to 15 g/day. 5.4.3

Soft Antiarrhythmic Agents

ACC-9358 Soft Analogs ACC-9358 (5-152, Figure 5-49) is an orally active class Ic antiarrhythmic agent that underwent clinical trials and for which a number of soft analogs (5-153) were synthesized and tested by Stout and co-workers at Du

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soft analog design

N O

N

HO

157

O

HO

O

R

n

NH N

n = 0, 1, 2, 3

N 5-152

5-153

ACC-9358

N

N

O O

HO

N

S

HO

O O

O

5-153b

O

N 5-153a

FIGURE 5-49. Soft antiarrhythmics: analogs (5-153) of ACC-9358 (5-152), an orally active class Ic antiarrhythmic agent that underwent clinical evaluation. Soft analogs such as 5-153a and 5-153b showed good activity and sufficiently short half-lives in human blood.

Pont [158]. Replacement of the formanilide function of ACC-9358 with alkyl esters resulted in compounds with similar antiarrhythmic activity. Esters attached directly to the aromatic ring of the bis(aminomethyl)phenol moiety (5-153, n = 0) were resistant to hydrolysis in human blood, but distancing the ester from the aromatic ring by one, two, and three methylene units [5-153, n = 1, 2, 3; R = − −CH2 CH(CH3 )2 ] afforded soft compounds with human blood half-lives of 8.7, 25.9, and 2.0 min, respectively [158]. As in most other cases, branching on the carbon attached to the oxygen atom of the alkoxy functionality tended to inhibit ester hydrolysis. The antiarrhythmic activity, as measured in vitro in the guinea pig right atrium, of a number of acid metabolites (5-153, n = 0, 1, 2; R = H) was indeed significantly less than that of the corresponding ester SDs. In a follow-up study, additional esters 5-153 derived from aromatic and heterocyclic alcohols were investigated to improve the lipophilic character and enhance the in vivo potency, biodistribution, and duration profile [480]. A number of them showed consistent ability to convert acetylstrophanthidin-induced arrhythmias in guinea pig right atria to normal sinus rhythm with an ED50 of less than 10 ␮g/mL. Based on their shorter half-life in human blood, esters 5-153a (t1/2 = 3.5 min) and 5-153b (t1/2 = 7.1 min) were selected for in vivo evaluation. They both demonstrated greater potency than lidocaine in the 24-h Harris dog model and equal potency to lidocaine in the oubaine-intoxicated dog model [480]. In addition, the lipophilicities of these compounds were lower than that of lidocaine, suggesting a lower ability to penetrate the blood–brain barrier and thus lower CNS liability. Considering all these observations, 5-153b was chosen as a potential development candidate, as it possessed the most desirable pharmacological and pharmacokinetic profile. Some analogs where the ester moiety was attached to the second aromatic ring of ACC-9358 [158] and some monoaminomethylene appended analogs were also explored [480].

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Budiodarone: An Amiodarone Soft Analog Amiodarone (5-154), a structural analog of thyroid hormone, has electrophysiological effects that among currently available antiarrhythmic agents, most closely resemble those of an ideal drug. However, amiodarone is highly lipophilic, is eliminated extremely slowly (having an elimination half-life of about 60 days), and has unusually complex PK properties, frequent side effects, and clinically significant interactions with many commonly used drugs [481]. Consequently, despite its high efficacy, amiodarone is used only for life-threatening ventricular antiarrhythmias that are refractory to other drugs. An active soft analog may solve many of these problems, and because amiodarone has a butyl side chain, its structure is well suited for such a design (Figure 5-50). A

O

O

O O S

O

5-154 amiodarone

I

O N H

5-157 dronedarone

I

O

O

N

N

O

soft analog design

O

O

R O

OH

O

O

O

O

O

O O

5-155 budiodarone (and analogs)

5-158 celivarone

I

I

I

O

I O

N

N 5-156

N

ATI-2000 a b c d e

R = Me (ATI-2001) R = Et (ATI-2010) R = iPr (ATI-2064) R = iBu R = sBu (budiodarone, ATI-2042)

f g h i j

R = nPe (ATI-2054) R = cyclohexyl R = (R)menthyl R = (R)endobornyl R = methyladamantane

FIGURE 5-50. Budiodarone (5-155e; ATI-2042) is a soft analog of amiodarone (5-154) and is the lead candidate selected for clinical development from a soft analog series (5-155), in which the butyl side chain at position 2 of the benzofurane moiety of amiodarone is replaced with an ester-containing side chain to allow facile hydrolytic degradation toward an inactive acid (5-156). Celivarone (5-158) is a soft analog of dronedarone (5-157) as well as amiodarone design following similar principles, with the ester moiety at the 5-position of the benzofuran ring replacing the corresponding methylsulfonamide group of dronedarone.

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number of possible soft analogs (ATI-2000 series, 5-155) were synthesized and tested for activity and duration of action [482–484]. For example, the electrophysiological effects of the first analog investigated, the methyl ester ATI-2001 (5-155a, R = CH3 ), were found to be even greater than those of amiodarone in guinea pig isolated heart, and in agreement with the SD design principles, they were more readily reversible. At equimolar concentration (1 ␮M), the soft analog caused significantly greater slowing of heart rate, depression of atrioventricular and intraventricular conduction, and prolongation of ventricular repolarization than amiodarone. However, unlike amiodarone, its effects were significantly reversed during washout of the drug. Because the half-life of 5-155a in human plasma was found to be only 12 min [485], which may be too short to allow long-term management of cardiac arrhythmias, esters with longer or more branched side chains were also examined. These modifications were found to markedly alter the magnitude and time course of the induced electrophysiological effects, and the sec-butyl and isopropyl esters were considered to merit further investigation [484]. In agreement with the principles of soft drug design, the common acid metabolite (5-156) was found to have no electrophysiological activity [484]. Budiodarone (ATI-2042; 5-155e), the sec-butyl analog, was selected for further clinical development as a possible treatment for atrial fibrillation (AF). A preliminary assessment of its effects in subjects with paroxysmal AF (PAF) and pacemakers has been performed using the electrocardiograph logs of advanced DDDRP pacemakers to monitor the efficacy [486]. Hence, this study used the sophisticated monitoring capacity of pacemakers to record all episodes of AF and differed from the conventional means of assessing drug efficacy by the “time to first recurrence” of AF. Six women with AF burden (AFB) between 1 and 50% underwent six sequential two-week study periods receiving increasing doses of budiodarone (0, 200, 400, 600, 800 mg b.i.d., and washout), and pacemaker data for the primary outcome measure AFB were downloaded during each period. Despite its limitations, this study suggested that budiodarone is safe, well tolerated, and can reduce AFB in patients with PAF since the mean reductions in AFB at all budiodarone doses were statistically significant (p < 0.005). A similar, but larger phase IIb study (PASCAL) comparing placebo and budiodarone (200, 400, and 600 mg b.i.d) in 72 patients with pacemakers and PAF also confirmed significant and dose-dependent AFB reduction of up to 75% over 12 weeks at the highest dose [487,488]. It should be noted that these compounds represent a possible example of an orally active SD. Even if the structures contain an ester moiety to allow enzymatic hydrolysis, it is possible to maintain activity for ester-containing drugs after oral administration. Indeed, many ester-containing drugs are orally administered. A study of the butyl ester prodrug of indomethacin in rats also showed that hydrolysis of the ester bond is carried out primarily in the circulatory system, and the bond is barely hydrolyzed in the intestinal tract [489]. The examples above illustrate the generally applicable isosteric/isoelectronic type of soft analog design, where an ester or reversed ester function replaces two neighboring methylene groups. In some of these cases, when sufficient structural variability is introduced, the distinction between a soft analog and a (real or hypothetical) inactive metabolite-based design may become somewhat blurred. For example, larger esters

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can no longer be regarded as strict structural analogs, but they can be regarded as esters of the (hypothetical) inactive acid metabolite 5-156. Celivarone: A Dronedarone (and Amiodarone) Soft Analog Dronedarone (5-157; Multaq, Sanofi-Aventis) is a noniodinated structural analog and an alternative to amiodarone (5-154) for the treatment of atrial fibrillation and atrial flutter in patients whose hearts have either returned to normal rhythm or who undergo drug therapy or electric shock treatment to maintain normal rhythm. The FDA approved dronedarone in 2009 for an indication of atrial fibrillation suppression, but not for the reduction of cardiovascular mortality among patients with atrial fibrillation [490]. Celivarone (SSR149744C; 5-158, Figure 5-50), also developed by Sanofi-Aventis, is a close analog of dronedarone with an isopropyl ester replacing the methylsulfonamide moiety of dronedarone. Hence, the metabolism of celivarone is through the hydrolysis of this ester function rather than CYP3A4, reducing its potential for drug–drug interactions [491], just as for any other soft drug. Animal studies have confirmed that celivarone has a multifactorial mechanism of action combining the blockade of several ion channels with the inhibition of responses of ␣1 and ␤1 adrenergic as well as AT1 receptor stimulation [492]. Due to its structural similarity, celivarone, similar to amiodarone, possesses the pharmacological effects of class I, II, III, and IV antiarrhythmic agents. Studies in rats and dogs confirmed that it is indeed an effective antiarrhythmic agent in atrial fibrillation and in ventricular arrhythmias being more potent than, or at least equipotent to, amiodarone and dronedarone [493]. Implantable cardioverter-defibrillators (ICDs) are the treatment of choice to prevent life-threatening arrhythmias. However, even in patients with ICDs, additional antiarrhythmic therapy is often required to reduce the morbidity associated with recurrent arrhythmia-triggered ICD interventions, and celivarone was evaluated for its possible therapeutic effect. One study (ICARIOS) in patients with ICDs investigating the efficacy of celivarone in the reduction of ventricular arrhythmia-triggered ICD interventions found a trend to reduce ventricular tachycardia- and ventricular fibrillation-triggered ICD therapies; however, this effect was not statistically significant [491]. There was a trend toward greater efficacy in the 300-mg celivarone group, especially in patients undergoing ICD therapy within 30 days prior to randomization. Celivarone was not associated with an increased risk of torsades de pointes, thyroid dysfunction, or pulmonary events. A larger study (ALPHEE) in 486 patients found that whereas celivarone had an acceptable safety profile, it was not effective for the prevention of ICD interventions or sudden death [494]. The proportion of patients experiencing an appropriate ICD intervention or sudden death was 61.5% in the placebo group; 67.0%, 58.8%, and 54.9% in the celivarone 50-, 100-, and 300mg/day groups, respectively; and 45.3% in the amiodarone group (200 mg/day after a loading dose of 600 mg/day for 10 days). 5.4.4

Soft Serotonin Receptor Agonists: Naronapride

Cisapride (5-159, Figure 5-51), a serotonin 5-HT4 receptor agonist, is a parasympathomimetic that increases muscle tone in the esophageal sphincter used in

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H2N

161

O O H N

Cl O

N

O

5-159 F

cisapride soft analog design H2N

O O H N

Cl

(S) (R)

O

O (R)

N

N

O

5-160 H 2N

naronapride

O

O H N

Cl O

(S) (R)

O N

OH 5-161 ATI-7500

FIGURE 5-51. Soft serotonin receptor agonists: cisapride (5-159) and its soft analog naronapride (ATI-7505, 5-160) designed to avoid CYP450-mediated metabolism and the QT prolongation side effects of cisapride.

gastroesophageal reflux disease (GERD) and in severe gastroparesis (e.g., in diabetic patients). In 2000, it has been withdrawn from the U.S. market due to drug-related pro-arrhythmic events (QT prolongation) occurring mainly in patients taking other medications that are known CYP3A4 inhibitors (e.g., erythromycin, fluconazole, amiodarone). Naronapride (ATI-7505; 5-160, Figure 5-51), an investigational 5-HT4 receptor agonist, was designed at ARYx Therapeutics as a soft analog to have similar gastro-prokinetic activity without the cardiac adverse effects by avoiding the CYP450 metabolism [495,496]. Metabolic studies in humans confirmed that naronapride is indeed extensively metabolized, undergoing rapid hydrolysis to its acid metabolite (ATI-7500; 5-161), which then undergoes further metabolic changes, including N-glucuronidation and side-chain oxidation [497]. The plasma terminal half-life of naronapride was 5.5 h, and the half-lives of its major metabolites ranged from 18 to 33 h. It is also noteworthy that unlike other members of its class, such as tegaserod and even cisapride, naronapride was found to have relevant affinity for only the 5-HT4 receptor subtype (Ki = 1.4 nM), negligible affinity for Kv 11.1 channels (Ki = 24 ␮M), and no effect on cardiac repolarization [497]. A randomized parallel-group double-blind placebo-controlled study in healthy volunteers designed to evaluate the effects of 9-day treatment on scintigraphic gastrointestinal and colonic transit found that naronapride (10 or 20 mg t.i.d.) accelerated

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gastric emptying, increased colonic transit, and accelerated ascending colon emptying [496]. This compound (5-160) has now also successfully completed phase II clinical trials for the treatment of multiple gastrointestinal disorders, including GERD and functional dyspepsia. In agreement with its SD design principles, a completed thorough QT study designed to test the cardiac safety of naronapride confirmed its safety profile by finding no significant effect on the QT interval at either therapeutic or supratherapeutic doses. Clinical studies also found that in addition to increasing gastric and colonic motility in healthy volunteers [496], oral naronapride also decreases gastric acid exposure to the esophagus and increases symptom-free days of nocturnal heartburn and dyspeptic symptoms in patients with symptomatic gastroesophageal reflux disease [497]. In a randomized placebo-controlled phase II study in patients with chronic idiopathic constipation, naronapride increased spontaneous bowel movements and decreased the time to the first bowel movement [497,498].

5.4.5

Soft Anticoagulants (Vitamin K Antagonists): Tecarfarin

Warfarin (5-162, Figure 5-52) and related 4-hydroxycoumarins are anticoagulants that decrease blood coagulation by inhibiting vitamin K epoxide reductase (VKOR), an enzyme that recycles oxidized vitamin K to its reduced form after it has participated in the carboxylation of several blood coagulation proteins, mainly prothrombin and factor VII. Hence, these drugs are also referred to as vitamin K antagonists. As its name indicates, warfarin was developed at the University of Wisconsin with funding from the Wisconsin Alumni Research Foundation, originally as a rat poison, being a derivative of dicoumarol, an anticoagulant identified in spoiled sweet clover–based animal feeds. It became a therapeutic drug serendipitously following observation of its relative safety in humans noticed following the unsuccessful attempt of a U.S.

O OH

O

O

5-162 warfarin soft analog design OH

OH

O O 5-163 tecarfarin

O O

CF 3 CF 3

OH O 5-164

O O

ATI-5900

FIGURE 5-52. Tecarfarin (5-163) designed as a soft analog of warfarin (5-162), a widely used anticoagulant.

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army cadet to commit suicide using warfarin. It is now the anticoagulant of choice to protect against stroke and other acute thrombotic events, and is the most widely prescribed oral anticoagulant drug in the United States. Nevertheless, despite its effectiveness, treatment with warfarin has several shortcomings. It has a high potential for drug–drug interactions, including some foods (particularly fresh plant-based foods containing vitamin K), and steady-state plasma levels of warfarin must be monitored to be maintained within a range that increases the blood clotting times to predetermined international normalized ratio (INR) values specific for particular clinical indications. Warfarin is an enantiomeric mixture with most of the pharmacological activity residing in the S-enantiomer. It is metabolized primarily by CYP2C9 and CYP3A4, therefore, being subject to interactions with drugs that inhibit or induce these CYP450 isoforms, resulting in either excessive or suboptimal anticoagulation, respectively [499]. Accordingly, a series of soft warfarin analogs have been designed at ARYx Therapeutics as possible oral anticoagulants less likely to be subject to such interactions [500–504]. The compound selected for clinical development is tecarfarin (ATI-5923; 5-163, Figure 5-52). Animal studies have shown that the primary mechanism of action of tecarfarin is VKOR inhibition, just as it is for warfarin [501]. The same study also confirmed that in beagle dogs, tecarfarin, contrary to warfarin, is not metabolized through the CYP450 system and does not interact with amiodarone, a drug frequently coadministered with warfarin in the treatment of atrial fibrillation. In dogs, once-daily oral doses of 0.5 mg/kg tecarfarin selectively reduced the levels of the vitamin K-dependent coagulation factors (factors II, VII, IX, and X) and prolonged the prothrombin time (PT) [503]. In rabbits, both tecarfarin and warfarin (1.0 and 1.5 mg/kg p.o., respectively, for two days) prolonged the PT, increased blood loss from ear incisions, and attenuated thrombus formation compared with the saline control. Tecarfarin has also been evaluated in clinical trials for the treatment of patients who are at risk for the formation of blood clots, such as those with atrial fibrillation or those at risk of venous thromboembolism. For example, tecarfarin has been evaluated in a multicenter, phase II, 6- to 12-week open-label study of 66 atrial fibrillation patients with a mild to moderate risk of stroke to determine its safety and tolerability and to ascertain an optimal dosing regimen [502]. After the initial three weeks of treatment, the mean interpolated time in therapeutic range was 71.4%, which compares favorably with TTR reported for warfarin in other trials and registries. The median daily dose to maintain an international normalized ratio between 2 and 3 was 15.6 mg. There was no evidence of accumulation of either tecarfarin (5-163) or its major metabolite 5-164 (ATI-5900) after 12 weeks of dosing, but there was variability of plasma concentrations, which was correlated with the VKORC1 genotype, with the GG, GA, and AA genotypes having highest, intermediate, and lowest concentrations, respectively [502]. Consistent with its design focusing on esterase-mediated metabolism, a recent clinical trial confirmed that whereas fluconazole, a CYP450 inhibitor, increases the plasma concentrations of both (R)- and (S)-warfarin, it does not increase that of tecarfarin [504]. This study with 24 healthy adult participants randomized 1 : 1 to

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receive approximately equipotent single oral doses of tecarfarin or warfarin (50 and 17.5 mg, respectively) found that the ratio of log-transformed mean AUC0-168 with versus that without fluconazole for tecarfarin was 91.2% (90% confidence interval, CI: 83.3 to 99.8) and for racemic warfarin was 213% (90% CI: 202 to 226) [504]. Whereas the 90 CI was entirely within the standard 80 to 125% interval required for bioequivalence [505] for tecarfarin, it was well outside it for warfarin. This confirms that there is a clinically significant PK interaction between warfarin and the CYP450 inhibitor fluconazole, whereas tecarfarin PK is unaffected by fluconazole. The group working on the development of budiodarone (5-155e), naronapride (5-160), and tecarfarin (5-163) has also been exploring several other possible SD designs, including such compounds as the following:

r Soft alosentron analogs as 5-HT3 receptor antagonists for the treatment of irritable bowel syndrome [506]

r Short-acting soft hypnotic barbiturates [507] r Soft fluvoxamine analogs as possible selective serotonin reuptake inhibitors for the treatment of depression [508]

r Soft mibefradil analogs as calcium-channel blockers [509], and others 5.4.6

Soft Angiotensin Converting Enzyme Inhibitors

ACE inhibitors such as captopril (5-165, Figure 5-53) or enalapril are widely accepted vasodilators in chronic heart failure. However, their use in acute conditions is restricted, owing to the prolonged duration of their effect. Again, a soft analog

HS

N O O

OH

5-165 captopril soft analog design HS

O

R

O

OH

O 5-166 S HS

S

O

HS

+

O 5-166a FPL 66564

HO

OH

O

O

OH 5-167

O

OH 5-168

FIGURE 5-53. Ultrashort-acting ACE inhibitors: design and metabolism of 5-166-type compounds as soft analogs of captopril (5-165). Hydrolytic cleavage of these compounds results in small hydrophilic metabolites (e.g., 5-167, 5-168) that have no ACE inhibitory activity.

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approach may provide an active, ultrashort-acting ACE inhibitor that may represent a workable solution. In work carried out by Baxter and co-workers at Fisons plc, UK based on these ideas, a number of captopril analogs (5-166, Figure 5-53) with the proline amide bond of 5-165 replaced by esters susceptible to hydrolytic cleavage were investigated [156]. Whereas no captopril hydrolysis was observed in human blood, a number of soft analogs, especially those with thioalkyl substituents, were degraded in a sufficiently fast manner. Potency, as measured on purified rabbit lung ACE, could be increased further with larger substituents, but the hydrolysis of these compounds became unacceptably slow. Soft analog 5-166a (FPL 66564) was selected as a potential drug candidate because it showed the required balance of ACE inhibitory potency (5.7 nM) and degradation rate (human blood t1/2 = 14 min). As required by general SD design principles, its hydrolytic products (5-167, 5-168) are without ACE inhibitor activity and, being small hydrophilic molecules, should not present any clearance problems. The in vivo testing of compound 5-166a showed a dose-dependent inhibition of angiotensin I pressor response in the anesthetized rat, but the effect dissipated rapidly to baseline levels on termination of the i.v. infusion [156]. 5.4.7

Soft Dihydrofolate Reductase Inhibitors

In work carried out at Uppsala University, Sweden and later also involving AstraZeneca, a series of esters were explored as possible dihydrofolate reductase (DHFR) inhibitors that are susceptible toward hydrolytic degradation [385,510–514]. DHFR is involved in the reduction of dihydrofolate to tetrahydrofolate, and reduced folates are important cofactors in the biosynthesis of nucleic acids and amino acids. Hence, DHFR inhibitors can limit cellular growth. Consequently, classical DHFR inhibitors such as methotrexate (5-169, Figure 5-54) or nonclassical DHFR inhibitors such as trimetrexate (5-170) have shown antineoplastic or antiprotozoal activity, and

NH2

NH2

O

NH

N

N

O

N

N

NH N

H2N

OH

N

H2N

O

N O

O O

5-169

OH

5-170

methotrexate

trimetrexate

soft analog design NH2

NH2

O

O O

N

O

N

O NH

H2N

OH

N 5-171

O O

OH

H2N

O N

O 5-172

O

FIGURE 5-54. Soft DHFR inhibitors: design of ester-containing soft analogs (5-171, 5-172) of methotrexate (5-169) and trimetrexate (5-170) as possible DHFR inhibitors.

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they could be useful in the treatment of inflammatory bowel disease (IBD), rheumatoid arthritis, psoriasis, and asthma. A number of compounds comprising a bicyclic aromatic unit connected by an ester-containing bridge to another aromatic ring (e.g., 5-171, 5-172) have been synthesized and investigated as possible therapeutic agents against Pneumocystis carinii pneumonia and IBD with increased safety. Substitution of the methyleneamino bridge common to antifolates with an ester-containing bridge (Figure 5-54) retained DHFR-inhibitory activity. The best ester-containing inhibitors were about 10 times less potent inhibitors than trimetrexate and piritrexim in the DHFR assay, but provided a slightly better pcDHFR selectivity index, which was defined as the ratio of IC50 (rat liver DHFR) to IC50 (P. carinii DHFR). Furthermore, the hydrolytic metabolites were all poor inhibitors. In vitro hydrolysis using human and rat tissues or available esterases were relatively slow for most of the esters. Human and rat liver fractions were more active than human duodenal mucosa and human blood leukocytes in hydrolyzing the compounds. Contrary to 5-171, effective in vivo hydrolysis and a favorable pharmacokinetic profile was found for 5-172 [511]. Finally, 5-171 exhibited good anti-inflammatory activity in a colitis model in mice [512], but showed unsatisfactory results in a rat arthritic model [510]. However, it showed some favorable in vivo effects at 15 mg/kg per day in a mouse colitis model [513]. 5.4.8

Soft Calcineurin Inhibitors (Soft Immunosuppressants)

Compounds such as cyclosporine (cyclosporin A) (5-173, Figure 5-55; also included as 1-15) and tacrolimus (FK506; 5-175, Figure 5-55) that block the signal transduction in T-cells by inhibiting the phosphatase calcineurin A are widely used to treat and prevent organ transplant rejection [515]. Cyclosporine has been a cornerstone of immunosuppression in transplantation since its introduction in the 1980s; it is, in effect, a prodrug that engages cyclophilin, an intracellular protein of the immunophilin family, forming a complex which then engages calcineurin [516]. Tacrolimus engages another immunophilin, FK506-binding protein 12 (FKBP12), to create a complex that inhibits calcineurin. Calcineurin inhibitors also have considerable therapeutic potential in the treatment of autoimmune diseases, such as asthma, psoriasis, atopic dermatitis, and rheumatoid arthritis. Unfortunately, long-term systemic exposure to such agents causes serious side effects, such as nephrotoxicity, increased blood pressure, neurotoxicity, and hypertrichosis [517]. Again, an SD approach might prevent this problem. Because of the possibility of local delivery by inhalation, soft calcineurin inhibitors are of particular interest for asthma, a chronic inflammatory disease in which the airways become hyperreactive and constrict easily after a variety of diverse stimuli. The disease is characterized by a pulmonary inflammatory cell infiltrate, consisting mainly of eosinophils, neutrophils, and mononuclear cells, particularly activated T-lymphocytes, and T-cells seem to be key effector cells in the inflammatory pathology associated with asthmatic airways. Soft Cyclosporine Analogs Cyclosporine (ciclosporin, cyclosporin A; Sandimmune, Neoral, Gengraf; 5-173) is a calcineurin inhibitor cyclic undecapeptide that

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O O

O N

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O

O

N

O

O O

O O

N

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N

H N

N

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O

O O

NH

O

H N

N

OH

N

O

N H

O

5-173

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5-175

cyclosporin A

tacrolimus (FK506) soft analog design

O

O

O O

HO O

O N

N

N

OH

O O

N

O

O H N

O O

N

O

O

O NH

O

O O

O

O

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N

O O

R = CH 3, CH 2CH 3, CH 2CH 2F, CH 2OCH 3, CH 2SCH3

O N

N

O O

OH O

5-174

5-176

soft cyclosporin Aanalogs

MLD987 (soft tacrolimus analog)

167

FIGURE 5-55 Soft calcineurin inhibitors: soft analogs of the calcineurin inhibitors cyclosporine (cylosporin A) (5-173, also 1-15) and tacrolimus (FK506, 5-175) designed as soft immunosuppressants intended as inhalation therapies for asthma.

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is widely used as an immunosuppressant in postallogeneic organ transplant to avoid organ rejection. A topical emulsion formulation (Restasis) is available for the treatment of keratoconjunctivitis sicca (dry eye). Cyclosporine showed promising results in the treatment of asthma even when given orally [518], especially when given by inhalation [519]. However, because its most serious side effect, nephrotoxicity, seems to be mechanism-based (i.e., caused by calcineurin inhibition), an SD approach may be particularly promising to identify a locally active but systemically nontoxic compound. Two series of drugs with an ester moiety inserted into the side chain of the main cyclosporine ring have been synthesized and tested by Lazarova and co-workers at Enanta Pharmaceuticals (5-174, saturated and unsaturated side chain, Figure 5-55) [520]. This unusual amino acid side chain is known to be essential (but not sufficient) for activity, and it is also the most accessible portion of the molecule for synthetic modifications. The olefin cross-metathesis reaction with a ruthenium catalyst provided an efficient one-step synthetic transformation route from cyclosporin A to the soft analogs 5-174 [520]. A number of simple ester derivatives of both the saturated and unsaturated side chains (5-174) were synthesized and tested for calcineurin inhibitory activity, together with the corresponding acid metabolites (5-174, R = H, Figure 5-55). Some of the unsaturated ester analogs showed activity similar to cyclosporine with IC50 s in the range 500 to 700 nM, while the saturated ester analogs were less active. Both acid metabolites (5-174, R = H) showed no calcineurin inhibition activity (IC50 > 5 ␮M), and a preliminary toxicology study confirmed the nontoxic nature of the unsaturated acid derivative. Hence, this approach may yield useful SD analogs of cyclosporine with considerable therapeutic potential for local activity with minimal systemic side effects. Soft Tacrolimus Analogs Along the same conceptual lines as for cyclosporine, a soft analog of tacrolimus (FK-506; Prograf, 5-175), another widely used calcineurin inhibitor, has also been explored as a potential candidate for the inhalation therapy of asthma by Hersperger and co-workers at the Novartis Institutes for BioMedical Research and Novartis Pharma Development in Switzerland [521]. The corresponding carboxylic ester incorporating compound, MLD987 (5-176, Figure 5-55), retained the potent immunosuppressant activity and inhibited the activation, proliferation, and release of cytokines from T-cells with IC50 values in the low nM range. In a rat model of allergic asthma, MLD987 reduced the influx of leukocytes into bronchoalveolar lavage fluid samples obtained from antigen-challenged animals when given into the airways by intratracheal administration (ED50 of 1 mg/kg) or by inhalation (ED50 of 0.4 mg/kg) while having an appreciably weaker activity when given orally or intravenously. The corresponding acid metabolite was less active in all assays. PK evaluations of 5-176 in rat and in rhesus monkey confirmed its low oral (2 to 4%) and pulmonary (7%, monkey) bioavailability. MLD987 administered intratracheally in actively sensitized BN rats challenged 3 h previously with ovalbumin, dose-dependently reduced the response to adenosine at doses that are not anti-inflammatory (hence, might reflect direct suppression of mast cell activation) [522]. These findings are consistent with a local action, making it likely to have a better TI than that of other selective T-cell inhibitors [521].

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O

H N

O N

Cl

Cl

N

Cl

N

S

5-177 R146225

O

N

soft analog design

H N

O

O N

a R = CH 3 b R = CH 2CH3 c R = (CH2 )n CH 3 d R = CH 2CH2 Ph e R = CH 2CF3 f R = CH 2CHCH 2 g R = (CH 2 )3OH h R = CH 2CN i R = CH 2COCH3 etc.

O N

Cl

N

H N

Cl

N

N

N

O S O

Cl 5-1 78

OH

S

R

O

Cl 5-1 79

FIGURE 5-56. Soft cytokine inhibitors (5-178) designed as lung-specific SDs inhibiting the production of IL-5.

5.4.9

Soft Cytokine Modulators

Soft IL-5 Inhibitors In an SD approach initiated at Johnson & Johnson Pharmaceutical Research toward the development of possible asthma medications, a series of triazinylphenylalkyl-thiazolecarboxylic acid esters that are inhibitors of the production of interleukin (IL)-5, a primary eosinophil-activating and proinflammatory cytokine, have been synthesized and evaluated [523]. Since the influx of leukocytes (eosinophils, lymphocytes, and monocytes) into the airways and their production of proinflammatory cytokines contribute to the severity of allergic asthma, a localized cytokine inhibitor is of considerable therapeutic interest, and inhaled SDs could provide the needed lung specificity. These structures, represented here by 5-178 (Figure 5-56) are loose structural analogs of R146225 (5-177), a novel, orally active inhibitor of IL-5 biosynthesis identified earlier [524], whose development was halted due to teratogenic effects. In fact, this could be considered an example of de novo SD design, as compounds were screened for a novel target, and close isosteric analogy was not a main guiding principle, as the design did not start from well-known existing drugs. Most 5-178 esters were rather resistant to metabolic conversion when incubated with lung S9 fraction, but several metabolized readily when incubated with liver S9 fraction. The combination of metabolic and activity data suggested the hydroxypropyl ester [5-178g, R = (CH2 )3 OH] as the most promising candidate. It showed good metabolic stability (t1/2 > 240 min) in human lung S9 fraction, but rapid (t1/2 = 15 min) conversion into the pharmacologically inactive carboxylic acid 5-179 in human liver preparations. In stimulated human whole blood cultures, it reduced not

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only the production of IL-5 (IC50 of 78 nM), but also the biosynthesis of the monocyte chemotactic proteins MCP-1 (IC50 of 220 nM), MCP-2 (580 nM), and MCP-3 (80 nM). Its intratracheal administration to allergic sheep (6 mg/animal), either before (–4 h) or after (+1.5 h) pulmonary allergen challenge, completely abrogated the late-phase airway response and reduced the bronchial hyperreactivity to inhaled carbachol while causing only short-lived and minimal plasma exposure, hence making it a promising candidate for the topical treatment of allergic disorders. Soft Toll-Like Receptor 7 Agonists as Localized Interferon-Inducing Agents Tolllike receptors (TLRs) are pattern recognition receptors that play a key role as part of the innate immune system by acting as microbial sensors [525]. They stimulate immune cells via the interleukin-1 (IL-1) receptor signaling pathway. TLRs are expressed on immune cells such as dendritic cells, monocytes, macrophages, and Blymphocytes, and exposure to microbes activates them via recognition of pathogenspecific components, resulting in the production of cytokines and expression of costimulatory molecules that modulate the adaptive immune response. Accordingly, Toll-like receptor 7 (TLR7) agonists are effective suppressors of Th2-derived inflammation and can be effective anti-inflammatory agents. However, systemic use could lead to systemic induction of various cytokines, such as IL-6, IL-12, and type I interferon (IFN), resulting in flu-like symptoms. Again, a soft drug approach could lead to localized anti-inflammatory compounds that lack the systemic side effects; one such approach focusing on soft adenine analogs as TLR7 agonists by Kurimoto and co-workers at Dainippon Sumitomo Pharma in Osaka, Japan and Murray and co-workers at AstraZeneca (UK) [526,527] is summarized here. Resiquimod (5-180, Figure 5-57) and imiquimod are imidazoquinoline-type IFN-inducing agents that are ligands for TLR7 and TLR8 and that have been considered as good drug candidates to suppress the Th2 cell–dependent immune response due to enhancement of the Th1 response. Resiquimod has been shown to inhibit allergen-induced airway inflammation and hyperreactivity by modulating Th1 and Th2 immune responses and has been considered as adjuvant for the specific immunotherapy of allergic disorders. This research group has identified adenine derivatives such as 5-181 as a novel class of IFN-inducing agents that could represent a novel class of agents for the treatment of allergic diseases by modulating Th1 and Th2 immune responses. To avoid the systemic side effects resulting from the induction of the above-mentioned cytokines (IL-6, IL-12, IFN), an SD approach was considered. From a whole series of possible soft analogs synthetized (5-182, Figure 5-57), two methyl esters 5-183 (SM-324405) and 5-185 (AZ12441970) were selected as promising candidates. Both compounds showed good TLR7 agonist activity (and no TLR8 activity) as assessed by their ability to activate NF␬B in a reporter cell line. Both compounds are rapidly metabolized into less active carboxylic acids: SM-324405 (5-183) into SM-324406 (5-184), with halflives (t1/2 ) of 2.6 min in human plasma and 0.19 min in rat plasma, and AZ12441970 (5-185) into AZ12443988 (5-186) even faster, with half-lives of 1.2 min in human plasma and 0.07 min in rat plasma. However, while the acid metabolite of 5-183 was, at best, only 10-fold less active than its parent ester in the reporter assay for human and rat TLR7, the corresponding acid metabolite of 5-185 was >60 fold less

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SOFT ANALOGS NH2

NH2 O

N

N

H N

N

N

O

O N

N

OH 5-180

5-181

resiquimod

adenine derivatives soft analog design

NH2

H N

N

O N

O

N m

k

5-182

n

O

NH2

m = 0, 1, 2 n = 0, 1

H N

N O

O

H N

N

O N

NH 2 O

O

N

OH

N

N

O O

O 5-184 SM-324406

5-183 SM-324405

NH2

NH 2 H N

N O

O N

H N

N

N

N

O

O N

N

N OH

O N 5-185 AZ12441970

N O

5-186 AZ12443988

O

FIGURE 5-57. Soft 8-oxoadenine derivatives (5-182) as Toll-like receptor 7 (TLR7) agonists. Compounds 5-183 (SM-324405) and 5-185 (AZ1241970) are promising potent TLR7 agonists (EC50 ≈ 50 to 150 nM) that are rapidly metabolized by human plasma (t1/2 ≈ 1 to 3 min) to pharmacologically less active carboxylic acids (5-184 and 5-186, respectively).

active than its parent ester, making AZ12441970 (5-185) a better soft drug candidate even if it lost some of the activity (ca. 160 nM vs. ca. 50 nM) [527]. When dosed into the lung, both compounds were metabolized rapidly and short-term exposure of the soft drug was sufficient to activate the IFN pathway. In a mouse allergic airways model, 5-185 showed efficacy in the suppression of antigen-induced eosinophilia and reduced bronchoalveolar lavage IL-5 levels at concentrations that induced either no, or negligible, levels of systemic cytokines, contrary to plasma nonmetabolizable compounds, which induced substantial systemic levels.

5.4.10

Soft Phosphodiesterase 4 Inhibitors

Cyclic nucleotide phosphodiesterase (PDE) enzymes are therapeutic targets for many diseases, and several nonspecific PDE inhibitors, such as theophylline, are used as approved therapeutics. Enzymes of the PDE4 subclass are the most prevalent PDEs in immune cells and are predominantly responsible for hydrolyzing cyclic

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SOFT DRUGS Cl

NH O

O

O

H N

O

O Cl

N

O 5-187

F

F

rolipram

5-188 rof lumilast

O

O

O

N

O B

B

CN

HO

CN

HO

5-189

5-190

AN2728

soft analog design

O

O

N

N

O

O O

B HO 5-191

O

OH

B HO

5-192

O

FIGURE 5-58. Known PDE4 inhibitors, including boron-containing compounds in clinical investigation (AN2728, 5-189), and the design of corresponding ester-containing soft analogs such as 5-191.

adenosine monophosphate (cAMP) within immune cells as well as in cells of the central nervous system. PDE4 was identified in the late 1980s as a feasible target for small-molecule inhibitors, with the chronic airway inflammation seen in asthma as a primary indication for this class of possible new anti-inflammatory drugs. However, most development candidates, such as rolipram (5-187, Figure 5-58), have been discontinued due to lack of efficacy and/or dose-limiting adverse events, with nausea, diarrhea, abdominal pain, vomiting, and dyspepsia being the most common [396]. Roflumilast (5-188), a selective, long-acting PDE4 inhibitor developed as an oral drug for the treatment of inflammatory conditions of the lungs, such as asthma and chronic obstructive pulmonary disease (COPD), was effective in clinical trials, but produced several dose-limiting side effects, including nausea, diarrhea, and headache; it was finally approved by the FDA in 2011 for reducing COPD exacerbations. A set of boron-containing PDE4 inhibitors, including AN2728 (5-189, Figure 5-58), have been selected for clinical development as anti-inflammatory agents for the topical treatment of psoriasis and atopic dermatitis [528]. Topical use is likely to minimize the possibility of systemic side effects compared to systemic use; however, to further improve the safety and the therapeutic index, an SD approach was also explored at Anacor Pharmaceuticals [529]. Corresponding ester analogs have been synthesized and tested; the ethyl ester 5-191 was selected as the most promising candidate, as it showed good inhibitory activity (IC50 = 47 nM vs. 490 nM for AN2728) and also less emetic activity than rolipram. Compound 5-191 showed broad

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173

SOFT ANALOGS HO

HO

NH

NH

N (R)

N

O P

OH P

esterase

R

HO

HO

O

O

(R)

H2 O

O

O O

O

OH P

NH

+

NH

5-193 O

a b c d

R = CH 2 CH 3 CH 2 CHF2 CH 2 CF3 CH 2 CH 2CF3

O 5- 194

5- 195 O

5- 196 inactive

FIGURE 5-59. Phosphonamide-based possible soft metalloproteinase inhibitors (5-193) investigated as antipsoriatic agents.

anti-inflammatory activity in a number of assays, including inhibition of cytokine release (TNF␣, IL-2, IFN-␥ , IL-5, IL-10) and inhibition of phorbol ester–induced mouse ear edema, whereas the acid metabolite 5-192 was essentially inactive. The SD 5-191 also converts rapidly to the corresponding acid in vitro in plasma and in vivo in mice. Hence, it is a promising PDE4 inhibitor for dermatological use. 5.4.11

Soft Matrix Metalloproteinase Inhibitors

A series of phosphonamide-based inhibitors showed potent (micromolar) inhibitory activity against the shedding of epidermal growth factors, amphiregulin, and heparinbinding EGF-like growth factor, which are suspected to participate in the development of psoriasis. However, because they also inhibited matrix metalloproteinases (MMPs), soft analogs (5-193, Figure 5-59) that are susceptible to hydrolytic inactivation were investigated at Nippon Organon to avoid systemic adverse effects such as the musculoskeletal syndrome of MMP inhibitors [530]. Activity was configuration sensitive; only the (R,R) configuration shown in 5-193 showed potent inhibition. In this series, hydrolysis of a phosphate ester and not that of a carboxylic ester was expected to provide metabolic inactivation. The P− −OEt ester bond of the ethyl ester turned out to be stable in human plasma, but di- and trifluoroethyl esters (5-193b, 5-193c) hydrolyzed rapidly (t1/2 of 10 and 4) can easily cross the BBB, while the hydrophilic intermediate (log D < 0) is no longer able to come out, providing a sustained release of the active estradiol (5-88).

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FIGURE 6-33. Comparison of the three-dimensional structures and electron distributions of the brain-targeting estradiol chemical delivery system (E2 -CDS) in its administered, neutral form (E2 -T, 6-75; left) and metabolically generated, quaternary form (E2 -T+ , 6-76; right). The electron-isodensity surfaces are for fully AM1-optimized structure. The color code changes gradually from blue, which corresponds to the more negative regions, to red, which corresponds to the more positive regions along the surface (0.01 electron/Å3 ≈ 0.0015 electron/bohr3 ).

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OH O

O

O

OH

OH hydrolysis (esterases)

OH HO

HO 5-91

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OH

inactive metabolite HO 5-89 estriol

OH

O

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HO

HO

5-90 estrone

5-88

O

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estradiol(E2) HO O

O (T+)

O

HO

O N

2-hydroxy-estrone ... glucuronide and sulfate conjugates

HO 6-75 E2-CDS

HO 6-76 metabolism

FIGURE 6-34. Soft drug (SD) and chemical delivery system (CDS) design approaches that form the RMDD loop with specific chemical structures for estradiol (E2 , 5-88), which has been subject to both of these approaches, together with the main metabolic pathways of E2 . (See text for full caption.)

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N

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OR RO O RO

OR

O

RO

5

OR

O

OR 2

4

O

3

OR

O

OR

1

O OR

O

O OR

OR

RO O

RO

O RO

RO O

RO O

8.487

O OR

O 6-77

α -CD ng = 6

RO

RO

RO

βCD 6-77a: R = H; 6-77b: R = H or CH2CH(OH)CH3; HPβCD + 6-77c: R = H or (CH2)4SO3-Na ; SBEβCD 9.818

β-CD ng = 7 11.963

γ-CD ng = 8

benzene ring (for size comparison)

FIGURE 6-45. Space-filling (CPK, Corey–Pauling–Koltun) structures showing fully AM1optimized geometries for natural ␣-, ␤-, and ␥ -cyclodextrins containing six, seven, and eight ␣-1,4-linked glucose units (ng ), respectively. A benzene ring is included for size comparison to illustrate the size of the interior cavities. A general chemical structure of ␤-cyclodextrin is shown in the top right corner (6-77); this includes natural ␤-cyclodextrin (6-77a), 2-hydroxypropyl-␤-cyclodextrin (6-77b, HP␤CD; Kleptose), sulfobutylether-␤-cyclodextrin (6-77c, SBE␤CD; Captisol), and other possibilities.

T

Targetor

S

Spacer

P

N-terminus

L Lipophilic Funconal Group

Pepde C-terminus

Trigonelline

Ala Ala-Ala Pro-Ala etc.

Cholesteryl Adamantyl, etc.

FIGURE 6-47. Molecular packaging: the molecular packaging strategy used for brain delivery of neuropeptides as an extension of the CDS approach.

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T

S

Trigonelline Ala

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P

L

Tyr-Ala-Gly-Phe-Leu

Cholesterol

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D T+

D F

4. Enzymac hydrolysis (dipepdyl pepdase) 1. Passive transport 2. Oxidaon

3. Enzymac hydrolysis (esterase/lipase) T+

T+

D

D

F

FIGURE 6-48. CPK (space-filling) models of structures, illustrating the sequential metabolism of the molecular package used for the brain delivery of a leucine enkephalin analog (D formed after step 4).

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FIGURE 6-51. Metabolic sequence required to release Leu2 -TRH (6-79) from its corresponding brain-targeting CDS molecular package (6-85). Calculated log partition and distribution coefficients (log P, log D) as well as molecular mass (MW) are included again to highlight the considerable changes in physicochemical properties during the metabolic sequence.

O

( S)

OH

OH

( S)

OH

( S)

H2N

H 2N

H 2N

k

k

N

N

O

O

HN NH2

NH 3

N

NH 2

NH 2

O

NH 2 6-93

L-Arg +

L-Lys +

6-94a Nys +

or

6-94

Lys(Nys +)

generalized Nys

6-95 +

generalized Nys

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H 2N 6-92

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H 2N

OH

( S)

( S)

H 2N

O

O

O

OH

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0.080 0.030 0.010 0.000 -0.010 -0.030 -0.060

brain-targeted redox analog (Tyr-Nys+)

FIGURE 6-56. See text for full caption.

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kyotorphine (Tyr-Arg)

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O O FL

NH2

H N

HN

N O NH2 9

6-102 FL-[aca-Nys+]9-NH2

FIGURE 6-59. Structure of the cell-penetrating molecular transporters with flexible backbones and permanently charged Nys+ side chains, FL [aca-Nys+ ]9 NH2 (6-102), and its AM1-optimized three-dimensional structure (without its FL fluorescein cargo) covered with soft, transparent surfaces colored according to the electrostatic potential. This time, more blue color corresponds to more positive regions.

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Cornea

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Conjuncva

Iris

T D

eliminaon administraon

Lens

T D Enzyme 1

F

F

CDS

T D Enzyme2

Opc nerve Rena

Vitreous body

D

FIGURE 6-60. Eye-targeting CDSs: schematic representation of the processes that provide site-specific targeting for the oxime-type CDS approach used for eye-targeted delivery of active ␤-blocker drugs (D).

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OH

OH O

OH

NH

R

O OH

O O

n

5-15 - 5-17

NH

hydrolysis (esterases)

n

O OH

O

5-11 O

known inactive metabolite

inactive metabolite (generalized structure) 5-15, adaprolol: n = 1,R = adamantylethyl 5-16, esmolol: n = 2,R = Me 5-17, landiolol: n = 2, R = (S-2, 2-dimethyl-1,3-dioxolan-4-yl)Me; morpholinocarbonylaminoethyl-amino derivative

soft β-blockers

OH NH

O OH

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5-10 O-demethylmetoprolol

OH NH

O O

soft drug design

OH O

5-7

O

20

metoprolol

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NH

5-9

OH

OH

N H N

OH

O O O

Rb

H N

O O O

O O

Rb OH

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α-hydroxymetoprolol

5-8 oxime (=NOH) or methoxime (=NOCH 3) derivative (inactive) metoprolol oxime: Rb = Me betaxoxime: R b = cyclopropylmethyl

6-104m ...

ketone derivative (inactive) metabolism

FIGURE 6-62. Another illustration of the soft drug (SD) and chemical delivery system (CDS) design approaches that form the RMDD loop with specific chemical structures—this time for metoprolol (5-7), which also has been subject to both of these approaches, together with its main metabolic pathways. Structures are arranged again in a manner similar to that of Figure 4-1, which provided an abstract, conceptual illustration of the RMDD loop (for space considerations, the orientation of the loop and its components has been rotated by 90◦ ).

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6-103m